A single RAG Agent handling all queries hits knowledge boundaries and performance bottlenecks. Multi-Agent RAG dispatches retrieval tasks to multiple specialized Agents, each with its own knowledge base and retrieval strategy, coordinated by a central Orchestrator that merges results.
Traditional RAG splits documents into small chunks for retrieval, but this causes information fragmentation. LongRAG leverages 100K+ token long-context models to retrieve larger document segments (entire sections or even whole documents), reducing fragmentation while maintaining retrieval efficiency.
Speculative RAG uses small specialist models to generate multiple answer drafts from different document subsets in parallel, then a large model verifies and selects the best answer in one pass. The paper reports +12.97 points accuracy and -50.83% latency on PubHealth — but that is the best cell in the table; other benchmarks gain far less.
Ollama wraps llama.cpp in a Docker-style CLI + REST API, letting you run LLMs locally with a single command. This post covers core concepts, installation, API, hardware requirements, Modelfile customization, and what this tool is — and isn't — good for.
RAG has evolved far beyond simple 'search + generate' into a technology ecosystem spanning ten generations — and since 2025 into an Agentic/Reasoning era. This article is a systematic navigation guide: from Naive RAG to Multi-Agent/LongRAG across ten generations, the post-ten Agentic Era (Search-R1/RL search, MCP, GraphRAG 3.x, vision-native retrieval), retrieval strategies, chunking, embedding, reranking, evaluation frameworks, observability, and cost optimization. Each topic has a dedicated deep-dive article.
vLLM uses PagedAttention to eliminate KV cache memory waste, combining continuous batching and prefix caching to become the most widely adopted open-source LLM inference engine today.
Building a chatbot is more than just calling an API. Conversation state management, memory mechanisms, streaming, guardrails, observability, and tech stack selection — every layer affects the user experience.
Good prompts aren't written in one go — they're iterated into existence. Start with the simplest prompt, test with real cases, classify error types, and make targeted fixes. This article covers the three-part System Prompt structure, reasoning framework selection, few-shot optimization, token budget management, and six common mistakes.
For complex multi-hop questions, a single RAG search isn't enough. Agentic RAG lets the LLM evaluate whether retrieved results are sufficient — if not, it rewrites the query and searches again, forming a ReAct loop.
Your choice of embedding model directly determines RAG search quality. BGE-M3's multilingual training, 1024-dimensional vectors, and matching Reranker make it a practical pick for Traditional Chinese RAG.
Chunks too large and retrieval loses precision; too small and you lose context; hit a table and retrieval falls apart entirely. Chunking is the most underrated part of RAG — pick the wrong strategy and no amount of downstream optimization will save you.
Bi-Encoders are too coarse, Cross-Encoders are too slow — ColBERT's Late Interaction finds the sweet spot: token-level comparison between query and document, but with document vectors that can be precomputed.
When you split a document into chunks, each chunk loses its place in the original document. Contextual Retrieval solves the isolated-chunk problem by generating a per-chunk context from the whole document and prepending it at index time.
Filters too strict and getting zero results? CRAG automatically relaxes them and retries — far better than letting the LLM hallucinate an answer from general knowledge.
Vector search similarity scores don't equal relevance. Cross-Encoders use pairwise comparison to reorder results and push the truly relevant documents to the top.
Vector search finds similarity; graph search traverses relationships. When a question requires reasoning across multiple entities — crag → route → sender → grade distribution — GraphRAG outperforms standard RAG.
Vector search handles semantics; BM25 handles keywords. Combining them with RRF is what lets you handle both fuzzy queries and exact terms at the same time.
Have an LLM generate an 'ideal answer' first, then embed that hypothetical document for search — it outperforms searching with the raw query.
After each conversation, asynchronously extract likely user preferences and skill level, then automatically personalize search parameters on the next query — no manual setup required.
Ranking purely by relevance leaves you with five documents all describing the same route. MMR strikes a balance between relevance and diversity, and layering in popularity weighting makes results even more useful.