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#llm

8 articles found

Risks of Running LLM-Generated Code with 'exec' Explained

The code can have harmful things in it (by chance, hallucinations or malice) and running it is a security risk. I see that there are other plotting libraries that can accept a json data holding all design orders, and generate the plot from it. no code. seems safer to me. Am I wrong? do proffesional

#llm#code execution#security risks#india tech#ai coding practices

· · DeepLearning.AI Updates

Monitor Key Logs for LLM Stream Failures: A Guide

I’ve been thinking about logging for streamed LLM responses, especially the cases where the response starts normally and then fails halfway through. For regular API calls, I usually log things like status code, latency, request id, and error type. But streamed LLM calls feel different because the fa

#llm#stream failures#logging#ai monitoring#india tech

· · InfoQ

Revolutionizing Marketing Forecasts: Target's LLM Innovations

Target built a generative AI system to improve marketing campaign forecasting by retrieving and ranking similar historical campaigns. Using embeddings, vector search, and LLM ranking, it replaces rule-based workflows. Evaluation shows 75% top-1 and 100% top-3 coverage. The system reduces manual effo

#ai#marketing forecasting#llm#target#india tech

· · Towards Data Science

Building LLM Knowledge Bases: A Comprehensive Guide

Use coding agents to power your knowledge base The post How to Build a Powerful LLM Knowledge Base appeared first on Towards Data Science.

#llm#knowledge base#ai development#india tech#natural language processing

· · Dev.to

Transforming Cloud Infrastructure: Advances in Adaptive Model Routing

This is part 3 of the Adaptive Model Routing series. Part 1 built an LLM categorizer with Groq — 8 categories, 3 tiers. Part 2 added k-NN embedding lookup in shadow mode, discovered 83% tier accuracy, and found 61% cost savings on paper. This post covers what happened next. When Phase 2 ended, I had

#cloud infrastructure#adaptive routing#llm#k-nn#india tech

· · Dev.to

Optimizing Calibration Set Size for LLM-as-Judge Applications

TL;DR. The human-labeled calibration set you use to validate an LLM-as-judge does not need a fixed size. It needs a size that depends on how balanced your labels are. For roughly balanced binary criteria with no heavy tail, 50 stratified traces will usually pin Cohen's kappa to within a tolerable ba

#calibration set#llm#ai validation#data science#india tech

· · VentureBeat

MeMo's Memory Model Revolutionizes LLM Upgrades, Boosts Performance

Enabling LLMs to acquire new knowledge after training remains a major hurdle for enterprise AI — current solutions are either too expensive, too slow, or constrained by context window limits. MeMo, a framework from researchers at multiple universities, encodes new knowledge into a dedicated smaller

#memo#llm#enterprise ai#continual learning#india ai

· · Dev.to

Unlocking LLM Quality: The Critical Role of Tokenizers

One Ruler to Measure Them All: How Language Affects LLM Quality Most discussions about LLM performance focus on the model architecture and prompting. But there's a hidden factor: the tokenizer. It determines how much of your text fits in the context window. Russian text consumes more tokens than Eng

#tokenizers#llm#multilingual#ai#india tech