AI RundownDaily
Topic

#RAG

2 articles — updated daily

Fine-Tuning vs Prompt Engineering vs RAG: A Builder's Guide
LLMs

Fine-Tuning vs Prompt Engineering vs RAG: A Builder's Guide

Fine-tuning, prompt engineering, and RAG each solve a different failure mode, and picking the wrong one is an expensive way to paper over a problem you never diagnosed. OpenAI's May 2026 wind-down of its self-serve fine-tuning platform, now down to a single model, and Anthropic's decision never to ship a public fine-tuning API both signal a market retreat from an over-used technique. The bigger signal: most 'let's fine-tune it' requests turn out to be knowledge gaps or instruction gaps in disguise, fixable with retrieval or a better prompt at a fraction of the cost. For PMs, the takeaway is a triage habit: diagnose whether you're facing stale facts, inconsistent format, or a genuinely novel reasoning task before you authorize a training run.

6 min read →
Continual Learning: Why Your Deployed LLM Still Can't Learn
Research

Continual Learning: Why Your Deployed LLM Still Can't Learn

Continual learning is the AI research goal of models that keep absorbing new knowledge after deployment without forgetting old skills — but no production LLM actually does this today. Frontier models like Claude Opus 4.8 and GPT-5.5 ship with fixed knowledge cutoffs and stay frozen until the next full retrain, months later; what looks like "learning" in products is really context windows, retrieval (RAG), and memory features re-injecting facts, not weight updates. The gap between the research narrative and shipped reality is widening as vendors add memory features that feel like learning but aren't. For PMs, this matters because any roadmap that assumes the model will organically get smarter about your users is building on a substitute, not the real thing.

5 min read →
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