<?xml version="1.0" encoding="UTF-8"?><rss xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:atom="http://www.w3.org/2005/Atom" version="2.0">
    <channel>
        <title><![CDATA[PromptQL Blog: The latest best from PromptQL!]]></title>
        <description><![CDATA[PromptQL is an AI analyst that captures your team's shared context, connects to data wherever it lives and writes code to get you answers.]]></description>
        <link>https://promptql.io/blog</link>
        <generator>RSS for Node</generator>
        <lastBuildDate>Thu, 16 Apr 2026 16:26:04 GMT</lastBuildDate>
        <atom:link href="https://promptql.io/blog/feed.xml" rel="self" type="application/rss+xml"/>
        <language><![CDATA[en]]></language>
        <item>
            <title><![CDATA[Same Question. Different Intelligence.]]></title>
            <description><![CDATA[There's a “tokenmaxxing” fever sweeping through AI teams right now. Engineers
flexing on leaderboards about how many tokens they can burn. Execs are starting
to catch on, throwing more compute at a problem doesn't magically make the
answers better.

But there's an opposite trap that's just as bad. Teams obsessing over per-token
cost, always reaching for the cheapest model, thinking they're being smart about
AI spend.

Both get it wrong.

What actually matters is: did you get the right answer, an]]></description>
            <link>https://promptql.io/blog/same-question-different-intelligence</link>
            <guid isPermaLink="true">https://promptql.io/blog/same-question-different-intelligence</guid>
            <dc:creator><![CDATA[Srini Sankar]]></dc:creator>
            <pubDate>Wed, 15 Apr 2026 06:15:05 GMT</pubDate>
        </item>
        <item>
            <title><![CDATA[150 Million Records. Zero Wait Time]]></title>
            <description><![CDATA[A Fortune 500 technology enterprise came to PromptQL with a problem every GTM
team knows: 150+ million records across Snowflake, cloud data warehouses, and
Salesforce — and business users who couldn't wait weeks for answers.

Every insight request joined a queue. By the time the answer arrived, the
decision window had closed.

"We have the data — what do we do from there? I can't sit around and wait for IT
and data platform teams to align on permissions."

The Problem
Data infrastructure wasn't ]]></description>
            <link>https://promptql.io/blog/150-million-records-zero-wait-time</link>
            <guid isPermaLink="true">https://promptql.io/blog/150-million-records-zero-wait-time</guid>
            <dc:creator><![CDATA[PromptQL Team]]></dc:creator>
            <pubDate>Thu, 09 Apr 2026 17:26:40 GMT</pubDate>
        </item>
        <item>
            <title><![CDATA[3 Silent Killers of Enterprise AI (And Why Your Pilot Will Probably Fail)]]></title>
            <description><![CDATA[Here's a pattern I keep seeing. Smart teams at smart companies spin up an AI
project. They pick a vendor. The demo looks great. Everyone's excited.

And then nothing happens.

The pilot sits there. Months pass. Eventually someone quietly kills it. Nobody
talks about why.

I've watched this play out enough times now that I can almost predict the
failure points before they happen. There are three of them. And weirdly, none of
them are about the AI being dumb.

Pain #1: The Access Control Nightmare]]></description>
            <link>https://promptql.io/blog/3-silent-killers-of-enterprise-ai-and-why-your-pilot-will-probably-fail-2</link>
            <guid isPermaLink="true">https://promptql.io/blog/3-silent-killers-of-enterprise-ai-and-why-your-pilot-will-probably-fail-2</guid>
            <dc:creator><![CDATA[PromptQL Team]]></dc:creator>
            <pubDate>Thu, 09 Apr 2026 17:26:10 GMT</pubDate>
        </item>
        <item>
            <title><![CDATA[Killing Slack was the only way to make AI accurate]]></title>
            <description><![CDATA[When we started to solve the problem of achieving AI that can work accurately on
internal data and be fully trusted, we took 3 big bets that we bet the farm on:

Bet 1: Coding will be human-grade reliable

AI writing code is the only way to achieve the degree of reliability,
repeatability and user-facing meta cognition that is trustworthy.

Instead of RAG, or tool-calling style approaches, we bet on making AI write code
to solve problems.

Bet 2: Multiplayer AI is the path to shared context

Acc]]></description>
            <link>https://promptql.io/blog/killing-slack-was-the-only-way-to-make-ai-accurate</link>
            <guid isPermaLink="true">https://promptql.io/blog/killing-slack-was-the-only-way-to-make-ai-accurate</guid>
            <dc:creator><![CDATA[PromptQL Team]]></dc:creator>
            <pubDate>Thu, 02 Apr 2026 13:15:46 GMT</pubDate>
        </item>
        <item>
            <title><![CDATA[I am always in a flow state now]]></title>
            <description><![CDATA[I used to spend my day navigating tools and tabs. Grafana, Slack, AWS,
Snowflake, GitHub, Linear—the list goes on. The work itself wasn't hard. The
friction was the work between the work, and that friction constantly broke my
flow state.

Now it feels different.

A few days ago, Rajoshi posted on Slack that new users weren't seeing the
"Connect Data" button. In the old world, that would've meant a rabbit hole:
clone the repo, grep through components, cross-reference with Linear, maybe
Slack some]]></description>
            <link>https://promptql.io/blog/i-am-always-in-a-flow-state-now</link>
            <guid isPermaLink="true">https://promptql.io/blog/i-am-always-in-a-flow-state-now</guid>
            <dc:creator><![CDATA[Shahidh K Muhammed]]></dc:creator>
            <pubDate>Tue, 31 Mar 2026 11:17:45 GMT</pubDate>
        </item>
        <item>
            <title><![CDATA[I am more confident (and less overwhelmed)]]></title>
            <description><![CDATA[Whatever the task, my starting point is “New thread” in PromptQL.
I’m not even exaggerating.

PromptQL has fundamentally changed how I work.

I’m in marketing, and it used to feel like half my job was just responding to
requests:

“How are we tracking on event registrations?”
“What asset should I send this prospect?”
“Can we launch a mini campaign on … ?”

Tackling these meant jumping between tools, cross-referencing and stitching
bits, pinging teammates, waiting for replies, copy-pasting links ]]></description>
            <link>https://promptql.io/blog/i-am-more-confident-and-less-overwhelmed</link>
            <guid isPermaLink="true">https://promptql.io/blog/i-am-more-confident-and-less-overwhelmed</guid>
            <dc:creator><![CDATA[Lili Riahi]]></dc:creator>
            <pubDate>Tue, 31 Mar 2026 11:17:29 GMT</pubDate>
        </item>
        <item>
            <title><![CDATA[I don’t keep a TODO list anymore]]></title>
            <description><![CDATA[Not because there’s less to do. But because the unit of work isn’t a task
waiting in line. It’s a thread already in motion.

Before PromptQL, work was a queue. Notice something, add it to a list, file a
ticket, wait, lose context, re-explain, wait again. Every step came with delay
and drift.

Now I don’t stack things for later. I start them immediately.

This isn’t multitasking. It’s a different operating model. Last Tuesday I had 14
threads going in parallel: 5 bug fixes, 2 feature development,]]></description>
            <link>https://promptql.io/blog/i-dont-keep-a-todo-list-anymore</link>
            <guid isPermaLink="true">https://promptql.io/blog/i-dont-keep-a-todo-list-anymore</guid>
            <dc:creator><![CDATA[Anushrut Gupta]]></dc:creator>
            <pubDate>Tue, 31 Mar 2026 11:17:05 GMT</pubDate>
        </item>
        <item>
            <title><![CDATA[43% accuracy with Opus-4.6 & friends - will Text-to-SQL ever be good enough?]]></title>
            <description><![CDATA[The recent Data Agent Benchmark (DAB) [https://arxiv.org/abs/2603.20576] by
Berkeley's EPIC data lab [https://epic.berkeley.edu/] shows disappointing
accuracy on state of the art frontier models on real world data questions.

ModelScoreOpus-4.643%Gemini-3-Pro38%GPT-5.225%Given the criticality of data to
run day to day business operations and make strategic business decisions, this
accuracy seems to indicate that AI is a far cry away from becoming useful with
internal data.

What's perhaps most s]]></description>
            <link>https://promptql.io/blog/berkeley-data-agent-benchmark-will-text-to-sql-ever-be-good-enough</link>
            <guid isPermaLink="true">https://promptql.io/blog/berkeley-data-agent-benchmark-will-text-to-sql-ever-be-good-enough</guid>
            <dc:creator><![CDATA[Tanmai Gopal]]></dc:creator>
            <pubDate>Wed, 25 Mar 2026 19:43:11 GMT</pubDate>
        </item>
        <item>
            <title><![CDATA[On shared context]]></title>
            <description><![CDATA[Companies run on shared context. Becoming AI-native requires systems that can capture, maintain, and apply that context for AI. In this article, we lay out the blueprint for building such a system.]]></description>
            <link>https://promptql.io/blog/on-shared-context</link>
            <guid isPermaLink="true">https://promptql.io/blog/on-shared-context</guid>
            <dc:creator><![CDATA[Tanmai Gopal]]></dc:creator>
            <pubDate>Wed, 18 Mar 2026 19:05:34 GMT</pubDate>
        </item>
        <item>
            <title><![CDATA[From Hours to Minutes: How a Global Payments Leader Is Transforming Fraud Investigation with PromptQL]]></title>
            <description><![CDATA[A Fortune 500 financial services company (operating across commercial payments,
cross-border transactions, and payment solutions across multiple continents)
came to PromptQL with a problem.

A problem that most fraud ops teams know intimately: too many alerts, not enough
analysts, and a manual investigation process that couldn't scale.

Their team processes more than 1,000 cases a day. Each one requires an analyst
to manually work through 80+ investigative questions across authentication logs,
t]]></description>
            <link>https://promptql.io/blog/from-hours-to-minutes-how-a-global-payments-leader-is-transforming-fraud-investigation-with-promptql</link>
            <guid isPermaLink="true">https://promptql.io/blog/from-hours-to-minutes-how-a-global-payments-leader-is-transforming-fraud-investigation-with-promptql</guid>
            <dc:creator><![CDATA[PromptQL Team]]></dc:creator>
            <pubDate>Mon, 02 Mar 2026 21:00:01 GMT</pubDate>
        </item>
        <item>
            <title><![CDATA[Unlocking Critical Data for Non-Technical Analysts at a Global Finance Company]]></title>
            <description><![CDATA[A leading global financial data and analytics firm sits on some of the most
valuable fixed income data in the market — curated third-party bond datasets, a
modern data infrastructure, and years of investment in data tooling.

But when a bond analyst wants to answer a question about a security, the data
isn't readily accessible. It's trapped behind tooling built for SQL experts, not
analysts.

PromptQL connects directly to the firm's existing data infrastructure and
surfaces answers in plain Engl]]></description>
            <link>https://promptql.io/blog/unlocking-critical-data-for-non-technical-analysts-at-a-global-finance-company</link>
            <guid isPermaLink="true">https://promptql.io/blog/unlocking-critical-data-for-non-technical-analysts-at-a-global-finance-company</guid>
            <dc:creator><![CDATA[PromptQL Team]]></dc:creator>
            <pubDate>Mon, 02 Mar 2026 20:59:46 GMT</pubDate>
        </item>
        <item>
            <title><![CDATA["The Fed Doesn't Wait: How a Global Bank Turned 1 Billion Records and a 5-Hour Bottleneck into an AI-Driven Reporting Workflow"]]></title>
            <description><![CDATA[Every morning, a small team at one of the world's largest financial institutions
opens the same reports. They're looking for movement — specifically, which line
items in a massive balance sheet shifted overnight, by how much, and why. Their
job is to validate that shift before publishing to the Federal Reserve.

It sounds straightforward. It is not.


--------------------------------------------------------------------------------

The Problem: Every Morning, Same Question, Half a Workday to Ans]]></description>
            <link>https://promptql.io/blog/the-fed-doesnt-wait-how-a-global-bank-turned-1-billion-records-and-a-5-hour-bottleneck-into-an-ai-driven-reporting-workflow</link>
            <guid isPermaLink="true">https://promptql.io/blog/the-fed-doesnt-wait-how-a-global-bank-turned-1-billion-records-and-a-5-hour-bottleneck-into-an-ai-driven-reporting-workflow</guid>
            <dc:creator><![CDATA[PromptQL Team]]></dc:creator>
            <pubDate>Mon, 02 Mar 2026 20:59:36 GMT</pubDate>
        </item>
        <item>
            <title><![CDATA[Company Culture as a Potluck, Not a Buffet]]></title>
            <description><![CDATA[As much as culture may be driven by company-initiated activities, we strongly believe it's not something that any one person or team can create or maintain on their own. ]]></description>
            <link>https://promptql.io/blog/company-culture-as-a-potluck</link>
            <guid isPermaLink="true">https://promptql.io/blog/company-culture-as-a-potluck</guid>
            <dc:creator><![CDATA[Melanie Marshall]]></dc:creator>
            <pubDate>Thu, 26 Feb 2026 23:02:19 GMT</pubDate>
        </item>
        <item>
            <title><![CDATA[The semantic layer is dead. Long live the wiki.]]></title>
            <description><![CDATA[Most “AI on data” programs are just semantic-layer maximalism with a new paint
job: if we can finally standardize meaning, the model will finally reason
accurately. 

A Wikipedia model is a better way to think about organizational meaning.It
won’t. A perfect semantic layer is neither sufficient nor operable. The
bottleneck is organizational semantics at runtime, not SQL.

 * Semantic layer is information-poor
 * Semantic layer operating model is flawed
 * The precedent: Wikipedia
 * Wiki implici]]></description>
            <link>https://promptql.io/blog/semantic-layer-dead-long-live-wiki</link>
            <guid isPermaLink="true">https://promptql.io/blog/semantic-layer-dead-long-live-wiki</guid>
            <dc:creator><![CDATA[Tanmai Gopal]]></dc:creator>
            <pubDate>Fri, 19 Dec 2025 22:02:17 GMT</pubDate>
        </item>
        <item>
            <title><![CDATA[Employee Spotlight: Arpit Kushwaha, Ultimate Team Player]]></title>
            <description><![CDATA[Since our early days as Hasura, we’ve indexed for hiring people who are driven
to keep learning, keep improving, and never say, that’s not in my job
description. After all, we know that real growth often happens outside the
lines, and careers don’t always follow a linear path. 

But what does that actually look like in practice? I sat down with Arpit
Kushwaha—who started his career as a Community Management intern many years ago
and now works as a Business Systems Support Specialist—to find out ]]></description>
            <link>https://promptql.io/blog/employee-spotlight-arpit-kushwaha</link>
            <guid isPermaLink="true">https://promptql.io/blog/employee-spotlight-arpit-kushwaha</guid>
            <dc:creator><![CDATA[Melanie Marshall]]></dc:creator>
            <pubDate>Tue, 25 Nov 2025 19:47:43 GMT</pubDate>
        </item>
        <item>
            <title><![CDATA[AI Analyst pyramid of needs]]></title>
            <description><![CDATA[A framework for enterprise AI Analyst implementation with a crawl-walk-run approach that maximizes adoption and impact.]]></description>
            <link>https://promptql.io/blog/ai-analyst-pyramid-of-needs</link>
            <guid isPermaLink="true">https://promptql.io/blog/ai-analyst-pyramid-of-needs</guid>
            <dc:creator><![CDATA[Asawari Samant]]></dc:creator>
            <pubDate>Fri, 19 Sep 2025 19:44:30 GMT</pubDate>
        </item>
        <item>
            <title><![CDATA[AI analysts are the future – but only if…]]></title>
            <description><![CDATA[How to solve the analytics bottleneck with AI, pros and cons of the various approaches, what goes into building a good AI Analyst, case studies, and how PromptQL can help. ]]></description>
            <link>https://promptql.io/blog/ai-analysts-are-the-future-but-only-if</link>
            <guid isPermaLink="true">https://promptql.io/blog/ai-analysts-are-the-future-but-only-if</guid>
            <dc:creator><![CDATA[Asawari Samant]]></dc:creator>
            <pubDate>Wed, 17 Sep 2025 12:45:34 GMT</pubDate>
        </item>
        <item>
            <title><![CDATA[Engineering-led consulting will shape the future Enterprise AI]]></title>
            <description><![CDATA[VentureBeat recently profiled our approach
[https://venturebeat.com/ai/promptqls-usd900-hour-ai-engineers-are-coming-for-mckinseys-ai-business] 
and asked whether engineering‑led firms are coming for the big consultancies’ AI
business. 

We're witnessing a technology that has had perhaps the largest percentage of
"stuck in pilot" in the last 30 years of continuous technology disruption. 95%
of enterprise AI projects are stuck in pilot - per a recent MIT study
[https://www.forbes.com/sites/jaimec]]></description>
            <link>https://promptql.io/blog/engineering-led-consulting-will-shape-future-of-enterprise-ai</link>
            <guid isPermaLink="true">https://promptql.io/blog/engineering-led-consulting-will-shape-future-of-enterprise-ai</guid>
            <dc:creator><![CDATA[Tanmai Gopal]]></dc:creator>
            <pubDate>Tue, 09 Sep 2025 22:07:12 GMT</pubDate>
        </item>
        <item>
            <title><![CDATA[Beyond Basic RAG: A Technical Deep Dive using DocsQL]]></title>
            <description><![CDATA[From Theory to Production: Implementing Smart Query Routing
In our previous post
[https://promptql.io/blog/beyond-basic-rag-promptqls-intent-driven-solution-to-query-inefficiencies]
, we explored why traditional RAG systems fail and introduced the concept of
intent-driven query architecture. Today, we're diving into the technical
implementation details of how we built this system using PromptQL for our own
documentation.

The Challenge: Transform our DocsQL system from a traditional RAG approach]]></description>
            <link>https://promptql.io/blog/beyond-basic-rag-a-technical-deep-dive-using-docsql</link>
            <guid isPermaLink="true">https://promptql.io/blog/beyond-basic-rag-a-technical-deep-dive-using-docsql</guid>
            <dc:creator><![CDATA[Rob Dominguez]]></dc:creator>
            <pubDate>Mon, 08 Sep 2025 15:02:11 GMT</pubDate>
        </item>
        <item>
            <title><![CDATA[Introducing the GenAI Assessment Framework (GAF): A 3×3 Matrix to Map Enterprise AI Needs]]></title>
            <description><![CDATA[Motivation
MIT’s GenAI Divide
[https://fortune.com/2025/08/18/mit-report-95-percent-generative-ai-pilots-at-companies-failing-cfo/] 
study found that 95% of generative AI pilots never make it past pilot. At the
same time, over 2,000 new AI startups got funded in 2024
[https://hai.stanford.edu/ai-index/2025-ai-index-report], 70,000+ new GenAI OSS
repositories created last year
[https://github.blog/news-insights/octoverse/octoverse-2024/], and almost every
single tech company has rebranded to "bei]]></description>
            <link>https://promptql.io/blog/durable-framework-evaluating-enterprise-ai</link>
            <guid isPermaLink="true">https://promptql.io/blog/durable-framework-evaluating-enterprise-ai</guid>
            <dc:creator><![CDATA[Anushrut Gupta]]></dc:creator>
            <pubDate>Sat, 06 Sep 2025 02:24:55 GMT</pubDate>
        </item>
        <item>
            <title><![CDATA[Evals 101 for executives: Your all-in-one AI PRD, roadmap, and ROI model]]></title>
            <description><![CDATA[Evals are the foundation for enterprise AI success. This post explains what evals are, why they matter now, what goes into a good eval set, and how to use them to estimate ROI before you scale.]]></description>
            <link>https://promptql.io/blog/evals-101-for-executives-your-all-in-one-ai-prd-roadmap-and-roi-model</link>
            <guid isPermaLink="true">https://promptql.io/blog/evals-101-for-executives-your-all-in-one-ai-prd-roadmap-and-roi-model</guid>
            <dc:creator><![CDATA[Asawari Samant]]></dc:creator>
            <pubDate>Fri, 05 Sep 2025 00:04:06 GMT</pubDate>
        </item>
        <item>
            <title><![CDATA[Launching GAT – An SAT for your AI]]></title>
            <description><![CDATA[We're launching a GAT design service to help AI change agents and leaders concretely define their GenAI initiatives and measure its progress.]]></description>
            <link>https://promptql.io/blog/launching-gats-sats-for-your-ai</link>
            <guid isPermaLink="true">https://promptql.io/blog/launching-gats-sats-for-your-ai</guid>
            <dc:creator><![CDATA[Tanmai Gopal]]></dc:creator>
            <pubDate>Thu, 04 Sep 2025 23:26:03 GMT</pubDate>
        </item>
        <item>
            <title><![CDATA[Static AI Is Already Cutting Jobs; What Happens When It Starts to Learn?]]></title>
            <description><![CDATA[From: Canaries in the coal mine, Brynjolfsson et al.

Most of the impact we’re seeing in the labor market is coming from mediocre AI.
That is significant. If baseline tools can move the needle, what happens when
systems start to learn on the job?

Recent Stanford research
[https://digitaleconomy.stanford.edu/wp-content/uploads/2025/08/Canaries_BrynjolfssonChandarChen.pdf] 
offers a clean early signal: since late 2022, early‑career workers (22–25) in
the most AI‑exposed occupations have experienc]]></description>
            <link>https://promptql.io/blog/continuously-learning-ai-impact-employment</link>
            <guid isPermaLink="true">https://promptql.io/blog/continuously-learning-ai-impact-employment</guid>
            <dc:creator><![CDATA[Tanmai Gopal]]></dc:creator>
            <pubDate>Thu, 28 Aug 2025 20:39:30 GMT</pubDate>
        </item>
        <item>
            <title><![CDATA[Beyond Basic RAG: Improving Your Knowledge Agents with Intent-Driven Architectures]]></title>
            <description><![CDATA[The RAG Bottleneck: When One Size Fits Nothing
Traditional Retrieval-Augmented Generation (RAG) systems suffer from a
fundamental inefficiency that's hiding in plain sight: They treat every query
identically, forcing simple questions through the same expensive embedding and
vector search pipeline as complex or technical requests.

Throughout this exploration of intent-driven architectures, we'll use a
documentation assistant as our primary example. But don't mistake this for a
post about documen]]></description>
            <link>https://promptql.io/blog/beyond-basic-rag-promptqls-intent-driven-solution-to-query-inefficiencies</link>
            <guid isPermaLink="true">https://promptql.io/blog/beyond-basic-rag-promptqls-intent-driven-solution-to-query-inefficiencies</guid>
            <dc:creator><![CDATA[Rob Dominguez]]></dc:creator>
            <pubDate>Mon, 18 Aug 2025 20:28:55 GMT</pubDate>
        </item>
        <item>
            <title><![CDATA[Being "Confidently Wrong" is holding AI back]]></title>
            <description><![CDATA[The failure mode that stalls “AI for data” efforts or "AI on my APIs" efforts isn’t psychedelic hallucination—it’s confident inaccuracy: plausible answers that are wrong in subtle and costly ways.]]></description>
            <link>https://promptql.io/blog/being-confidently-wrong-is-holding-ai-back</link>
            <guid isPermaLink="true">https://promptql.io/blog/being-confidently-wrong-is-holding-ai-back</guid>
            <dc:creator><![CDATA[Tanmai Gopal]]></dc:creator>
            <pubDate>Mon, 18 Aug 2025 20:17:28 GMT</pubDate>
        </item>
        <item>
            <title><![CDATA[The ultimate guide to semantic layers for AI]]></title>
            <description><![CDATA[Enterprise AI analytics often fail due to missing business context. A semantic layer can help. We explore what to look for in a AI-ready semantic layer and how PromptQL's approach can address the gap. ]]></description>
            <link>https://promptql.io/blog/the-ultimate-guide-to-semantic-layers-for-ai</link>
            <guid isPermaLink="true">https://promptql.io/blog/the-ultimate-guide-to-semantic-layers-for-ai</guid>
            <dc:creator><![CDATA[Asawari Samant]]></dc:creator>
            <pubDate>Fri, 15 Aug 2025 18:32:41 GMT</pubDate>
        </item>
        <item>
            <title><![CDATA[Partnering with UC Berkeley to Build the Benchmark Enterprise AI Needs]]></title>
            <description><![CDATA[AI reliability needs a new kind of benchmark.

Most benchmarks today test techniques — not the questions real businesses ask.
They don't reflect the messy, siloed data or the pressure to get it right the
first time.

That's why we're excited to partner with University of California, Berkeley's
EPIC Data Lab and Professor Aditya Parameswaran to change that.

Together, we're building the first benchmark for AI data agents focused on
enterprise reliability — grounded in real-world datasets from fin]]></description>
            <link>https://promptql.io/blog/partnering-with-uc-berkeley-to-build-the-benchmark-enterprise-ai-needs</link>
            <guid isPermaLink="true">https://promptql.io/blog/partnering-with-uc-berkeley-to-build-the-benchmark-enterprise-ai-needs</guid>
            <dc:creator><![CDATA[Anushrut Gupta]]></dc:creator>
            <pubDate>Tue, 08 Jul 2025 00:07:27 GMT</pubDate>
        </item>
        <item>
            <title><![CDATA[Architectural Limitations of Probabilistic Tool Selection]]></title>
            <description><![CDATA[Abstract
LLM tool calling has become a popular architectural approach as agents move from
demos to full-scale deployment. However, practitioners report unpredictable
failures in production. To understand these failure patterns, we analyzed a
sample of GAIA benchmark questions using publicly available tool-calling AI
agents (Manus AI, H20 AI) compared against PromptQL's code-first approach. Our
evaluation reveals tool calling often fails due to i) inconsistent
problem-solving, ii) interpretation ]]></description>
            <link>https://promptql.io/blog/architectural-limitations-of-probabilistic-tool-selection</link>
            <guid isPermaLink="true">https://promptql.io/blog/architectural-limitations-of-probabilistic-tool-selection</guid>
            <dc:creator><![CDATA[Tobi Ogunnaike]]></dc:creator>
            <pubDate>Mon, 07 Jul 2025 23:56:19 GMT</pubDate>
        </item>
        <item>
            <title><![CDATA[Achieving Perfect AI Accuracy on Database & Numerical Tasks]]></title>
            <description><![CDATA[Abstract
We demonstrate that PromptQL achieves 100% accuracy on the Database Querying &
Numerical Computation tasks of the CRMArena-Pro benchmark—a result that
dramatically outperforms state-of-the-art approaches which typically achieve
30-60% accuracy on complex enterprise tasks. This breakthrough stems from
PromptQL's fundamental architectural innovation: the separation of query
planning from execution, enabling deterministic and explainable AI operations on
enterprise data. In this post, we a]]></description>
            <link>https://promptql.io/blog/achieving-perfect-ai-accuracy-on-database-and-numerical-tasks</link>
            <guid isPermaLink="true">https://promptql.io/blog/achieving-perfect-ai-accuracy-on-database-and-numerical-tasks</guid>
            <dc:creator><![CDATA[Anushrut Gupta]]></dc:creator>
            <pubDate>Mon, 07 Jul 2025 23:14:56 GMT</pubDate>
        </item>
        <item>
            <title><![CDATA[Fundamental Failure Modes in RAG Systems]]></title>
            <description><![CDATA[Abstract
Retrieval-Augmented Generation (RAG) has emerged as a critical approach for
enhancing Large Language Models (LLMs) with external knowledge. However,
traditional RAG implementations demonstrate significant accuracy limitations
when handling complex, multi-hop reasoning tasks. This paper presents a
comprehensive evaluation of three RAG approaches using the FRAMES benchmark
[https://huggingface.co/datasets/google/frames-benchmark] dataset, revealing
that PromptQL's plan-based execution met]]></description>
            <link>https://promptql.io/blog/fundamental-failure-modes-in-rag-systems</link>
            <guid isPermaLink="true">https://promptql.io/blog/fundamental-failure-modes-in-rag-systems</guid>
            <dc:creator><![CDATA[Anushrut Gupta]]></dc:creator>
            <pubDate>Mon, 07 Jul 2025 22:04:30 GMT</pubDate>
        </item>
        <item>
            <title><![CDATA[How a F100 consumer brand transformed supply chain intelligence with PromptQL]]></title>
            <description><![CDATA[How PromptQL delivered fast, trusted insights across global supply chain systems at a F100 consumer enterprise









]]></description>
            <link>https://promptql.io/blog/how-promptql-transformed-supply-chain-intelligence-for-a-f100-consumer-brand</link>
            <guid isPermaLink="true">https://promptql.io/blog/how-promptql-transformed-supply-chain-intelligence-for-a-f100-consumer-brand</guid>
            <dc:creator><![CDATA[PromptQL Team]]></dc:creator>
            <pubDate>Fri, 27 Jun 2025 21:06:38 GMT</pubDate>
        </item>
        <item>
            <title><![CDATA[The data-readiness myth: Is your semantic layer holding your AI back?]]></title>
            <description><![CDATA[AI shouldn't need perfect data - just the ability to learn. Meet the agentic semantic layer that adapts.









]]></description>
            <link>https://promptql.io/blog/your-semantic-layer-is-holding-your-ai-back</link>
            <guid isPermaLink="true">https://promptql.io/blog/your-semantic-layer-is-holding-your-ai-back</guid>
            <dc:creator><![CDATA[Anushrut Gupta]]></dc:creator>
            <pubDate>Fri, 27 Jun 2025 20:46:37 GMT</pubDate>
        </item>
        <item>
            <title><![CDATA[Solve these 6 hidden architecture problems to turn your data warehouse into an AI powerhouse]]></title>
            <description><![CDATA[
Your data team has built impressive Snowflake infrastructure. Clean pipelines,
well-governed tables, strong security controls. The business is excited about AI
possibilities.

Yet your AI agent may still misclassify your biggest enterprise client as a
high-risk customer. Why does this happen?

Why doesn’t the agent understand what you mean by “high-value prospects"? And
how do  simple  questions return different answers each time they're asked?

The problem isn't your data quality or your model]]></description>
            <link>https://promptql.io/blog/solve-these-6-hidden-architecture-problems-to-turn-your-data-warehouse-into-an-ai-powerhouse</link>
            <guid isPermaLink="true">https://promptql.io/blog/solve-these-6-hidden-architecture-problems-to-turn-your-data-warehouse-into-an-ai-powerhouse</guid>
            <dc:creator><![CDATA[PromptQL Team]]></dc:creator>
            <pubDate>Thu, 26 Jun 2025 22:39:22 GMT</pubDate>
        </item>
        <item>
            <title><![CDATA[Beyond the booth: What is "100% accurate" enterprise AI?]]></title>
            <description><![CDATA[We dissect PromptQL's 100% accuracy claim and show the architecture that turns this provocative hyperbole into operational guarantees.]]></description>
            <link>https://promptql.io/blog/beyond-the-booth-what-is-100-accurate-enterprise-ai</link>
            <guid isPermaLink="true">https://promptql.io/blog/beyond-the-booth-what-is-100-accurate-enterprise-ai</guid>
            <dc:creator><![CDATA[Tanmai Gopal]]></dc:creator>
            <pubDate>Thu, 26 Jun 2025 16:51:16 GMT</pubDate>
        </item>
        <item>
            <title><![CDATA[Expose the Data Prep Tax: A Path to High-Impact AI Deployment]]></title>
            <description><![CDATA[Every company pays a hidden "data prep tax" - weeks of setup and constant upkeep. Eliminate this with PromptQL to unlock the curiosity and innovation that's been dormant for years.]]></description>
            <link>https://promptql.io/blog/expose-the-data-prep-tax-a-path-to-high-impact-ai-deployment</link>
            <guid isPermaLink="true">https://promptql.io/blog/expose-the-data-prep-tax-a-path-to-high-impact-ai-deployment</guid>
            <dc:creator><![CDATA[Tanmai Gopal]]></dc:creator>
            <pubDate>Tue, 10 Jun 2025 14:12:36 GMT</pubDate>
        </item>
        <item>
            <title><![CDATA[Find the Unanswered Questions: A Path to High-Impact AI Deployment]]></title>
            <description><![CDATA[$100M lost to unanswered questions. Reliable AI that speaks the language of your business lets every employee ask and act instantly.]]></description>
            <link>https://promptql.io/blog/find-the-unanswered-questions-a-path-to-high-impact-ai-deployment</link>
            <guid isPermaLink="true">https://promptql.io/blog/find-the-unanswered-questions-a-path-to-high-impact-ai-deployment</guid>
            <dc:creator><![CDATA[Tanmai Gopal]]></dc:creator>
            <pubDate>Sat, 07 Jun 2025 02:14:20 GMT</pubDate>
        </item>
        <item>
            <title><![CDATA[Spot the Automation Paradox: A Path to High-Impact AI Deployment]]></title>
            <description><![CDATA[$200M lost to hidden automation gaps. AI that speaks expert logic can unlock massive value.








]]></description>
            <link>https://promptql.io/blog/spot-the-automation-paradox-a-path-to-high-impact-ai-deployment</link>
            <guid isPermaLink="true">https://promptql.io/blog/spot-the-automation-paradox-a-path-to-high-impact-ai-deployment</guid>
            <dc:creator><![CDATA[Tanmai Gopal]]></dc:creator>
            <pubDate>Sat, 07 Jun 2025 02:09:03 GMT</pubDate>
        </item>
        <item>
            <title><![CDATA[Conversational Analytics at Scale: How a Global Restaurant Chain Unlocked Revenue Insights with PromptQL]]></title>
            <description><![CDATA[How PromptQL helped a global restaurant brand instantly turn plain English questions into trusted actionable insights. 






]]></description>
            <link>https://promptql.io/blog/conversational-analytics-at-scale-how-a-global-restaurant-chain-unlocked-revenue-insights-with-promptql</link>
            <guid isPermaLink="true">https://promptql.io/blog/conversational-analytics-at-scale-how-a-global-restaurant-chain-unlocked-revenue-insights-with-promptql</guid>
            <dc:creator><![CDATA[PromptQL Team]]></dc:creator>
            <pubDate>Mon, 02 Jun 2025 01:22:13 GMT</pubDate>
        </item>
        <item>
            <title><![CDATA[Building an AI-powered GTM operating system: A F100 success story with PromptQL]]></title>
            <description><![CDATA[How a F100 giant unified CRM data and then layered AI to reimagine their GTM engine.







]]></description>
            <link>https://promptql.io/blog/using-ai-to-power-the-gtm-operating-system-with-promptql-from-hasura</link>
            <guid isPermaLink="true">https://promptql.io/blog/using-ai-to-power-the-gtm-operating-system-with-promptql-from-hasura</guid>
            <dc:creator><![CDATA[PromptQL Team]]></dc:creator>
            <pubDate>Mon, 12 May 2025 16:30:00 GMT</pubDate>
        </item>
        <item>
            <title><![CDATA[Using AI agents to maximize Open Banking value]]></title>
            <description><![CDATA[Discover how AI and metadata-driven architecture empower financial institutions to turn Open Banking from a compliance task into a strategic advantage.]]></description>
            <link>https://promptql.io/blog/using-ai-agents-to-maximize-open-banking-value</link>
            <guid isPermaLink="true">https://promptql.io/blog/using-ai-agents-to-maximize-open-banking-value</guid>
            <dc:creator><![CDATA[Kenneth Stott]]></dc:creator>
            <pubDate>Tue, 06 May 2025 15:28:34 GMT</pubDate>
        </item>
        <item>
            <title><![CDATA[Rapidly delivering explainable product recommendations for Credit Unions with PromptQL]]></title>
            <description><![CDATA[Credit unions need to make smart, trustworthy product recommendations, but traditional AI tools fall short on speed and explainability. With PromptQL, a leading Fintech platform transformed how they deliver personalized, transparent recommendations in days, not months.







]]></description>
            <link>https://promptql.io/blog/rapidly-delivering-explainable-product-recommendations-for-credit-unions-with-promptql</link>
            <guid isPermaLink="true">https://promptql.io/blog/rapidly-delivering-explainable-product-recommendations-for-credit-unions-with-promptql</guid>
            <dc:creator><![CDATA[PromptQL Team]]></dc:creator>
            <pubDate>Mon, 28 Apr 2025 18:24:19 GMT</pubDate>
        </item>
        <item>
            <title><![CDATA[Bridging AI and enterprise data with PromptQL x MCP]]></title>
            <description><![CDATA[This post explores how our newly released PromptQL MCP server enhances the MCP ecosystem while enabling powerful new AI-data interaction patterns for enterprises]]></description>
            <link>https://promptql.io/blog/bridging-ai-and-enterprise-data-with-promptql-x-mcp</link>
            <guid isPermaLink="true">https://promptql.io/blog/bridging-ai-and-enterprise-data-with-promptql-x-mcp</guid>
            <dc:creator><![CDATA[Anushrut Gupta]]></dc:creator>
            <pubDate>Wed, 23 Apr 2025 19:33:14 GMT</pubDate>
        </item>
        <item>
            <title><![CDATA[The AI value gap: Why today's enterprise AI fails at the 20% that matters most]]></title>
            <description><![CDATA["AI Value Gap" – the growing disconnect between the business-critical operations where AI could deliver extraordinary ROI and the actual capabilities of current AI implementations.]]></description>
            <link>https://promptql.io/blog/the-ai-value-gap-why-todays-enterprise-ai-fails-at-the-20-that-matters-most</link>
            <guid isPermaLink="true">https://promptql.io/blog/the-ai-value-gap-why-todays-enterprise-ai-fails-at-the-20-that-matters-most</guid>
            <dc:creator><![CDATA[Praveen Durairaju]]></dc:creator>
            <pubDate>Wed, 09 Apr 2025 18:17:59 GMT</pubDate>
        </item>
        <item>
            <title><![CDATA[How PromptQL achieves 100% accuracy for AI on enterprise data]]></title>
            <description><![CDATA[PromptQL addresses the limitations of traditional approaches like RAG, Text-to-SQL, and Tool Calling. PromptQL provides measurable improvements in accuracy (with a clear path to reaching 100% accuracy) when connecting LLMs to enterprise data.]]></description>
            <link>https://promptql.io/blog/how-promptql-achieves-100-accuracy-for-ai-on-enterprise-data</link>
            <guid isPermaLink="true">https://promptql.io/blog/how-promptql-achieves-100-accuracy-for-ai-on-enterprise-data</guid>
            <dc:creator><![CDATA[Praveen Durairaju]]></dc:creator>
            <pubDate>Tue, 11 Mar 2025 19:59:59 GMT</pubDate>
        </item>
        <item>
            <title><![CDATA[Building a powerful customer support AI assistant with PromptQL in 5 minutes]]></title>
            <description><![CDATA[In today's data-driven world, connecting AI to your organization's data remains a significant challenge. What if you could build AI assistants that can query databases, access customer support tickets, and even take actions—all using natural language?]]></description>
            <link>https://promptql.io/blog/building-a-powerful-customer-support-ai-assistant-with-promptql-in-5-minutes</link>
            <guid isPermaLink="true">https://promptql.io/blog/building-a-powerful-customer-support-ai-assistant-with-promptql-in-5-minutes</guid>
            <dc:creator><![CDATA[Praveen Durairaju]]></dc:creator>
            <pubDate>Thu, 27 Feb 2025 00:39:20 GMT</pubDate>
        </item>
        <item>
            <title><![CDATA[Integrate real-time web search and enrich your private data with PromptQL]]></title>
            <description><![CDATA[By integrating web search in PromptQL, you can enrich your private data – stored in any systems like Snowflake, BigQuery, PostgreSQL, MongoDB, or accessed through APIs – with current real-time details from the web.]]></description>
            <link>https://promptql.io/blog/integrate-real-time-web-search-and-enrich-your-private-data-with-promptql</link>
            <guid isPermaLink="true">https://promptql.io/blog/integrate-real-time-web-search-and-enrich-your-private-data-with-promptql</guid>
            <dc:creator><![CDATA[Praveen Durairaju]]></dc:creator>
            <pubDate>Mon, 24 Feb 2025 22:18:42 GMT</pubDate>
        </item>
        <item>
            <title><![CDATA[Build safer AI assistants with PromptQL and human-in-the-loop guardrails]]></title>
            <description><![CDATA[One of the most effective guardrails in AI systems today is human-in-the-loop (HITL). This blog explores why HITL systems are crucial, how they align with the principles of agentic AI, and how PromptQL simplifies their implementation in the AI Assistant interface.]]></description>
            <link>https://promptql.io/blog/build-safer-ai-assistants-with-promptql-human-in-the-loop-guardrails</link>
            <guid isPermaLink="true">https://promptql.io/blog/build-safer-ai-assistants-with-promptql-human-in-the-loop-guardrails</guid>
            <dc:creator><![CDATA[Praveen Durairaju]]></dc:creator>
            <pubDate>Tue, 21 Jan 2025 19:47:46 GMT</pubDate>
        </item>
        <item>
            <title><![CDATA[What’s new in PromptQL: Managed LLM keys, Collaboration and Automation with APIs]]></title>
            <description><![CDATA[Over the past few weeks, we’ve introduced a series of exciting updates to PromptQL that enhance its power, accessibility, and collaboration capabilities. These enhancements are focused on simplifying workflows. Everything is now live and ready to explore!]]></description>
            <link>https://promptql.io/blog/whats-new-in-promptql-managed-llm-keys-collaboration-and-automation-with-apis</link>
            <guid isPermaLink="true">https://promptql.io/blog/whats-new-in-promptql-managed-llm-keys-collaboration-and-automation-with-apis</guid>
            <dc:creator><![CDATA[Praveen Durairaju]]></dc:creator>
            <pubDate>Tue, 14 Jan 2025 23:17:54 GMT</pubDate>
        </item>
        <item>
            <title><![CDATA[Introducing PromptQL Program API: Dynamic Integrations made simple]]></title>
            <description><![CDATA[With the PromptQL Program API, you can now trigger PromptQL programs via HTTP, bringing flexibility and intelligence to your business workflows. The API ensures seamless integration outside the chat interface of the PromptQL Playground.]]></description>
            <link>https://promptql.io/blog/introducing-promptql-program-api-dynamic-integrations-made-simple</link>
            <guid isPermaLink="true">https://promptql.io/blog/introducing-promptql-program-api-dynamic-integrations-made-simple</guid>
            <dc:creator><![CDATA[Praveen Durairaju]]></dc:creator>
            <pubDate>Thu, 09 Jan 2025 22:50:16 GMT</pubDate>
        </item>
        <item>
            <title><![CDATA[How Hasura took Leonardo.Ai from first code commit to production in 30 days]]></title>
            <description><![CDATA[“There's no way we could have got to the market as quickly as we did without it,” he said. “We definitely got to market at least twice as fast because of Hasura.”  ]]></description>
            <link>https://promptql.io/blog/how-hasura-took-leonardo-ai-from-first-code-commit-to-production-in-30-days</link>
            <guid isPermaLink="true">https://promptql.io/blog/how-hasura-took-leonardo-ai-from-first-code-commit-to-production-in-30-days</guid>
            <dc:creator><![CDATA[PromptQL Team]]></dc:creator>
            <pubDate>Mon, 08 Jan 2024 13:41:24 GMT</pubDate>
        </item>
    </channel>
</rss>