#retrieval-augmented-generation

2 posts tagged with #retrieval-augmented-generation

Every article below is hand-written, technically reviewed, and focused on retrieval-augmented-generation. Posts cover real-world architecture decisions, code-level implementation patterns, and trade-offs you'll only discover after shipping production systems.

machine learning python code embeddings nlp screen — illustration for article on RAG Context Window Limits: AI and Machine Learning

RAG Context Window Limits: Why Bigger Is Not Better [2026]

Expanding your context window from 4K to 128K tokens doesn't fix RAG — it masks retrieval failures with coherent-sounding hallucinations. Here's the measurement framework that actually works.

14 min read
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LangChain vs LlamaIndex 2026: Which LLM Framework Should You Pick? AI and Machine Learning

LangChain vs LlamaIndex 2026: Which LLM Framework Should You Pick?

LangChain wins for building complex, multi-step AI agents and conversational workflows; LlamaIndex wins for production-grade RAG pipelines and data-heavy retrieval systems. Choose based on whether your app is agent-first or retrieval-first.

11 min read
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