Tag: ai-concepts
All the articles with the tag "ai-concepts".
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Why LLMs forget everything — and what you must do about it
Every LLM call is stateless. The model has no memory of previous turns unless you explicitly provide them. This post explains why, what the context window limit means for conversations, and the three strategies for managing memory in AI applications.
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Improving RAG quality — reranking and hybrid search
Vector search retrieves semantically similar chunks, but similarity alone doesn't guarantee relevance. Reranking scores retrieved candidates by true relevance. Hybrid search adds keyword matching to catch exact terms. Together they meaningfully improve RAG answer quality.
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Chunking strategy in RAG — the decision that silently kills answer quality
How you split documents before indexing determines whether your RAG pipeline retrieves useful context or useless fragments. Chunk too large and embeddings average out. Chunk too small and context is missing. This post covers the tradeoffs.
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What is RAG and why your AI app almost certainly needs it
LLMs know a lot, but they don't know about your business. RAG — Retrieval-Augmented Generation — fixes this by retrieving relevant documents at query time and injecting them into the prompt. Here is why it exists and when to use it.
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Vector databases explained — why regular databases are not enough for AI
Semantic search requires finding the nearest neighbours among millions of high-dimensional vectors. PostgreSQL with B-tree indexes was not built for this. Here is what vector databases do differently and which options work with Spring AI.