Analytics Vidhya

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[Analytics Vidhya @AnalyticsVidhya]

Jev costs $0.044 per 1,000 judgements.

GPT-6 Astra costs $12.182. But cheaper isn't always better: Jev matches on simple evidence checks and trails on harder reasoning.

Best setup? Use both.
https://t.co/PbEi1zIaSU #AIEvals #LLM

[Analytics Vidhya @AnalyticsVidhya]

Your RAG bot can still make things up, even with the source text right in front of it 🫠

So let the LLM write 5 answers and let JEV, a tiny, cheap checker, grade each one on 4 yes/no questions.

🚫 Blocked all 16 planted wrong answers
💸 56 checks cost about $0.001
⚠️ But it also blocked ~1/3 of the good ones

Full breakdown 👇 https://t.co/hHvfEy1Ic8

[Analytics Vidhya @AnalyticsVidhya]

Closed-source app. No docs. One AI agent, 7 minutes, $1.83 🕵️

We tested REA, an open-source MCP server for reverse engineering. First we guessed how Obsidian’s quick-switcher ranks results by hand, then let the agent inspect the code.

🎯 It found the scoring function inside a 3.9MB minified file
✅ Our hand-made prediction was right on priority order, even the exact “a word boundary = 10 positions” trade-off
🔍 It also found 4 hidden mechanisms we couldn’t see from outside
⚠️ Its written formula had an off-by-one that only showed up when checked against its own data
Lesson: predict first, then verify.

Full breakdown 👇 https://t.co/t8awP2Bi4h

[Analytics Vidhya @AnalyticsVidhya]

FDE or AI Engineer?

Both build with AI.

The difference: where you sit, and who owns the system after it ships.

New to the FDE path? Start with our free course: https://t.co/sDlyXxlusO #AICareers #AIEngineer

[Analytics Vidhya @AnalyticsVidhya]

Your RAG bot can still make things up, even with the source text right in front of it 🫠

Fix: let the LLM write 5 answers, then let JEV (a tiny, cheap checker) ask 4 yes/no questions about each: supported? on-topic? invented details? contradictions?

🚫 Blocked all 16 planted wrong answers
💸 56 checks cost about $0.001
⚠️ But it also blocked ~1/3 of the good answers
🤷 Writing 5 drafts didn't help, the first one was already right
Start with 1 draft + 1 JEV check, then measure before scaling up.

Full breakdown 👇 https://t.co/hHvfEy2g1G

[Analytics Vidhya @AnalyticsVidhya]

We put Gemini 4 Argon through 4 real tasks 🧪

🎬 8-second product video: nailed it
🌐 Working booking website in Canvas: nailed it
📉 "Conversion fell, roll it back": it spotted that both visitor groups actually improved
📄 8 conflicting launch claims: all labeled correctly, but one invented phrase slipped into the summary 👀

Great at catching traps, still needs a proofread.

Full review 👇 https://t.co/AeM1KfyhaG

[Analytics Vidhya @AnalyticsVidhya]

OpenAI's new API doesn't chat, it decides 🎯

✅ Yes/no with probability
🧭 Pick from options, with odds
📏 Grade on a rubric

10x faster, $0.10 per 1M tokens, text and images. And it knows when it's unsure: a half-markets, half-regulation headline scored just 52% 😅

https://t.co/uk3ChbhAuM

[Analytics Vidhya @AnalyticsVidhya]

Your GPU is 99% idle while generating tokens. Yes, really 😳

LLM decode is stuck waiting on memory, not math. The fix is batching:

🚌 Static: waits for the slowest request, one 500-token reply holds up everyone
⏳ Dynamic: starts sooner, still waits to finish
⚡ Continuous: swaps in new requests every single token
Our simulator: continuous batching finished the same work in 41% fewer steps. A 30-token request waited 320 steps on static, exactly 30 on continuous.

Full breakdown 👇 https://t.co/Gfx7mTOfxN

[Analytics Vidhya @AnalyticsVidhya]

The best AI system won’t be the one with the model you trust most.

It’ll be the one where you can trust the model the least. 🎯

[Analytics Vidhya @AnalyticsVidhya]

We’re giving AI agents our most sensitive data and the power to act on our behalf.

Can anyone explain why a model did what it did? Nope. 😅

Here’s the plan: trust the model the least. A thread 🧵

[Analytics Vidhya @AnalyticsVidhya]

The checklist:

🔀 Use different models, none grades its own homework
👁️ Log everything in tamper-proof, human-readable form
🧪 Test the failures, not just the wins
🛑 Keep an emergency brake that always works
📢 When things break, tell everyone what failed

[Analytics Vidhya @AnalyticsVidhya]

Your RAG system confidently answers from the wrong documents. Here's the fix 🔧

Agentic Corrective RAG adds a safety net:

🧐 An LLM grades every retrieved chunk: relevant or not
🔁 Irrelevant? It rewrites the query and searches the web for live info
🧩 Built in LangGraph with Chroma + Tavily, nodes and edges you can follow
Fewer hallucinations, fresher answers.

Full guide 👇 https://t.co/oeKIEtvb0v

[Analytics Vidhya @AnalyticsVidhya]

Nano Banana 2.1 is here and free for everyone 🍌

We threw 4 tests at it:

📊 Bar chart with exact values: proportions came out spot on
🧱 Clay-style RAG explainer: clean, though one of the "two books" went missing
🖼️ Hawa Mahal as a linocut print: gorgeous style, but it deleted the busy street
💻 Debugging comic: consistent character, unreadable typo
Great for charts and comics. Always check the details you can't afford to lose.

Full review 👇 https://t.co/cLxuAbS9aT

[Analytics Vidhya @AnalyticsVidhya]

Your LLM judge says "0.995 confident" on every single answer. Useful? Not really 😬

Jev is a tiny judge model with honest probabilities:

💸 277x cheaper and 13x faster than GPT-6 Astra on the CMU study
🎯 Matches it on fact-checking, falls apart on math, code and logic
🪜 Smart trick: trust Jev when it's confident, send only the unsure cases to a bigger LLM. 93.4% accuracy at 41% of the cost
In our test, the one case Jev got wrong was also its lowest-confidence call.

Full comparison 👇 https://t.co/6VSotcQblF

[Analytics Vidhya @AnalyticsVidhya]

Most agent bugs aren't in the answer, they're in the dozens of tiny decisions before it 🎯

Jev turns those into typed choices with probabilities. 10 GitHub repos show what you can build with it:

🌐 Browser agents that pick actions from a table
✂️ Context pruning for coding agents
🔍 A code reviewer that scores severity
🧭 A router that sends easy prompts to cheaper models
Full list + repos 👇 https://t.co/oemzJUNSWC

[Analytics Vidhya @AnalyticsVidhya]

We built a real enterprise support agent with Claude Fable 5.1 🏗️

🕵️ 8 prompts: investigation layer, policy RAG, human approval gates, security attacks, multi-tenancy
🚫 Refund limits stayed hardcoded, never an LLM call
💉 Tried a prompt injection hidden in customer text, it stayed just text
🐛 Planted a bug, Claude Code couldn't reproduce it and said so

Full build 👇 https://t.co/JjYOSKRZWl