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"value": "What are advanced RAG patterns like HyDE, RAG-Fusion, FLARE, and Self-RAG? Give me code examples for at least two of them."
},
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"value": "Advanced RAG patterns address the core weaknesses of naive retrieval: query-document mismatch, single-query blind... | {
"category": "rag_architecture",
"context": "advanced RAG patterns"
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"value": "What are advanced RAG patterns like HyDE, RAG-Fusion, FLARE, and Self-RAG? Give me code examples for at least two of them."
},
{
"from": "gpt",
"value": "Advanced RAG patterns address the core weaknesses of naive retrieval: query-document mismatch, single-query blind... | {
"category": "rag_architecture",
"context": "advanced RAG patterns"
} | a555f125-1071-4d15-af0b-bb73e3694628 |
[
{
"from": "human",
"value": "What is GraphRAG and how do knowledge graphs enhance retrieval compared to standard vector search?"
},
{
"from": "gpt",
"value": "GraphRAG combines vector similarity search with graph traversal, enabling retrieval of information that requires understanding entity rel... | {
"category": "rag_architecture",
"context": "GraphRAG and knowledge graph-enhanced retrieval"
} | 5e2b2cb1-56ef-436a-a1b9-6baa1465b1db |
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"value": "How do I handle different document types — PDFs, HTML pages, code files, and tables — when ingesting data into a RAG system?"
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"value": "Different document types require specialised loaders and parsers. Using the wrong one loses structure (table ce... | {
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"context": "handling PDF, HTML, code, and tables in RAG pipelines"
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"value": "Explain the main chunking strategies for RAG: fixed-size, semantic chunking, recursive character splitter, and parent-child chunks. When should I use each?"
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"value": "Chunking is one of the highest-leverage decisions in RAG. The wrong strategy cau... | {
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"context": "chunking strategies for RAG"
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"value": "What is contextual compression in RAG and how do I use LLMChainExtractor and EmbeddingsFilter to reduce context window usage?"
},
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"value": "Contextual compression sits between retrieval and generation. Instead of passing entire chunks verbatim to the... | {
"category": "contextual_compression",
"context": "compressing retrieved context to reduce LLM costs"
} | a983627f-151e-48d4-a5bd-956d77f0c479 |
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"value": "How do I choose between text-embedding-3-small, BGE embeddings, sentence-transformers, and Cohere Embed for my RAG system?"
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"value": "Embedding model choice affects retrieval quality, latency, and cost more than any other single component. Here is... | {
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"context": "comparing embedding models for RAG"
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"value": "How do I optimize a RAG system for production — caching, latency, and cost at scale?"
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"value": "Production RAG at scale has three main cost centres: embedding API calls, vector search latency, and LLM generation tokens. Here is how to tackle each.... | {
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"value": "What is GraphRAG and how do knowledge graphs enhance retrieval compared to standard vector search?"
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"value": "GraphRAG combines vector similarity search with graph traversal, enabling retrieval of information that requires understanding entity rel... | {
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"value": "Compare Pinecone, Weaviate, ChromaDB, and pgvector for RAG. How do I connect to each from Python?"
},
{
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"value": "Each vector database targets a different part of the spectrum from developer convenience to enterprise scale. Here is a practical comparis... | {
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"context": "choosing and using vector databases"
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"value": "Compare Pinecone, Weaviate, ChromaDB, and pgvector for RAG. How do I connect to each from Python?"
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"value": "Each vector database targets a different part of the spectrum from developer convenience to enterprise scale. Here is a practical comparis... | {
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"value": "Compare Pinecone, Weaviate, ChromaDB, and pgvector for RAG. How do I connect to each from Python?"
},
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"value": "Each vector database targets a different part of the spectrum from developer convenience to enterprise scale. Here is a practical comparis... | {
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"value": "Explain the main chunking strategies for RAG: fixed-size, semantic chunking, recursive character splitter, and parent-child chunks. When should I use each?"
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"value": "Chunking is one of the highest-leverage decisions in RAG. The wrong strategy cau... | {
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"value": "How do I evaluate my RAG system using RAGAS? Walk me through faithfulness, answer relevancy, and context recall metrics with code."
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"value": "RAGAS (Retrieval Augmented Generation Assessment) provides reference-free and reference-based metrics tha... | {
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"value": "How do I implement multi-hop RAG for questions that require chaining multiple retrieval steps? Give me a concrete example."
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"value": "Multi-hop RAG answers questions like \"What is the population of the country where the CEO of OpenAI was born?\" ... | {
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"value": "How does hybrid search work in RAG? Show me how to combine BM25 sparse retrieval with dense embeddings using Reciprocal Rank Fusion."
},
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"value": "Hybrid search combines keyword-based BM25 (sparse) with semantic embedding search (dense). Each method ... | {
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{
"from": "human",
"value": "Compare Pinecone, Weaviate, ChromaDB, and pgvector for RAG. How do I connect to each from Python?"
},
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"value": "Each vector database targets a different part of the spectrum from developer convenience to enterprise scale. Here is a practical comparis... | {
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"value": "Different document types require specialised loaders and parsers. Using the wrong one loses structure (table ce... | {
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"value": "What are advanced RAG patterns like HyDE, RAG-Fusion, FLARE, and Self-RAG? Give me code examples for at least two of them."
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"value": "What are advanced RAG patterns like HyDE, RAG-Fusion, FLARE, and Self-RAG? Give me code examples for at least two of them."
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"value": "Advanced RAG patterns address the core weaknesses of naive retrieval: query-document mismatch, single-query blind... | {
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"value": "What is GraphRAG and how do knowledge graphs enhance retrieval compared to standard vector search?"
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"value": "GraphRAG combines vector similarity search with graph traversal, enabling retrieval of information that requires understanding entity rel... | {
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"value": "How do I choose between text-embedding-3-small, BGE embeddings, sentence-transformers, and Cohere Embed for my RAG system?"
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"value": "Embedding model choice affects retrieval quality, latency, and cost more than any other single component. Here is... | {
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"value": "A Retrieval-Augmented Generation (RAG) pipeline has five core stages: document loading, chunking, embedding, retriev... | {
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"value": "My RAG system has poor answer quality. How do I diagnose whether the problem is low recall, hallucinations, or context stuffing, and fix each?"
},
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"value": "Poor RAG quality always traces back to one of three failure modes: the right document was nev... | {
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"value": "Explain the main chunking strategies for RAG: fixed-size, semantic chunking, recursive character splitter, and parent-child chunks. When should I use each?"
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"value": "Chunking is one of the highest-leverage decisions in RAG. The wrong strategy cau... | {
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"value": "How do I evaluate my RAG system using RAGAS? Walk me through faithfulness, answer relevancy, and context recall metrics with code."
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"value": "RAGAS (Retrieval Augmented Generation Assessment) provides reference-free and reference-based metrics tha... | {
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"value": "GraphRAG combines vector similarity search with graph traversal, enabling retrieval of information that requires understanding entity rel... | {
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{
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{
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"value": "GraphRAG combines vector similarity search with graph traversal, enabling retrieval of information that requires understanding entity rel... | {
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{
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"value": "GraphRAG combines vector similarity search with graph traversal, enabling retrieval of information that requires understanding entity rel... | {
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"context": "GraphRAG and knowledge graph-enhanced retrieval"
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{
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"value": "My RAG system has poor answer quality. How do I diagnose whether the problem is low recall, hallucinations, or context stuffing, and fix each?"
},
{
"from": "gpt",
"value": "Poor RAG quality always traces back to one of three failure modes: the right document was nev... | {
"category": "retrieval_evaluation",
"context": "debugging RAG failures — low recall, hallucinations, context stuffing"
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"value": "How do I evaluate my RAG system using RAGAS? Walk me through faithfulness, answer relevancy, and context recall metrics with code."
},
{
"from": "gpt",
"value": "RAGAS (Retrieval Augmented Generation Assessment) provides reference-free and reference-based metrics tha... | {
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{
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"value": "How do I choose between text-embedding-3-small, BGE embeddings, sentence-transformers, and Cohere Embed for my RAG system?"
},
{
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"value": "Embedding model choice affects retrieval quality, latency, and cost more than any other single component. Here is... | {
"category": "embedding_models",
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"value": "Production RAG at scale has three main cost centres: embedding API calls, vector search latency, and LLM generation tokens. Here is how to tackle each.... | {
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"value": "Compare Pinecone, Weaviate, ChromaDB, and pgvector for RAG. How do I connect to each from Python?"
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{
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"value": "Each vector database targets a different part of the spectrum from developer convenience to enterprise scale. Here is a practical comparis... | {
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"value": "RAGAS (Retrieval Augmented Generation Assessment) provides reference-free and reference-based metrics tha... | {
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"value": "Poor RAG quality always traces back to one of three failure modes: the right document was nev... | {
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"value": "How do I choose between text-embedding-3-small, BGE embeddings, sentence-transformers, and Cohere Embed for my RAG system?"
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{
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"value": "Embedding model choice affects retrieval quality, latency, and cost more than any other single component. Here is... | {
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"context": "comparing embedding models for RAG"
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"value": "How do I build a RAG system that generates responses with inline citations linking back to source documents?"
},
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"value": "Citation generation ensures every claim in the answer is traceable to a specific source, which is critical for legal, medical, ... | {
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"value": "Production RAG at scale has three main cost centres: embedding API calls, vector search latency, and LLM generation tokens. Here is how to tackle each.... | {
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"value": "Multi-hop RAG answers questions like \"What is the population of the country where the CEO of OpenAI was born?\" ... | {
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"value": "Compare Pinecone, Weaviate, ChromaDB, and pgvector for RAG. How do I connect to each from Python?"
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"value": "Each vector database targets a different part of the spectrum from developer convenience to enterprise scale. Here is a practical comparis... | {
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"value": "Explain the main chunking strategies for RAG: fixed-size, semantic chunking, recursive character splitter, and parent-child chunks. When should I use each?"
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"value": "Chunking is one of the highest-leverage decisions in RAG. The wrong strategy cau... | {
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"value": "Poor RAG quality always traces back to one of three failure modes: the right document was nev... | {
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{
"from": "human",
"value": "How do I choose between text-embedding-3-small, BGE embeddings, sentence-transformers, and Cohere Embed for my RAG system?"
},
{
"from": "gpt",
"value": "Embedding model choice affects retrieval quality, latency, and cost more than any other single component. Here is... | {
"category": "embedding_models",
"context": "comparing embedding models for RAG"
} | a91adae8-d6bd-409e-b6ce-3bf77fd57c57 |
[
{
"from": "human",
"value": "What is GraphRAG and how do knowledge graphs enhance retrieval compared to standard vector search?"
},
{
"from": "gpt",
"value": "GraphRAG combines vector similarity search with graph traversal, enabling retrieval of information that requires understanding entity rel... | {
"category": "rag_architecture",
"context": "GraphRAG and knowledge graph-enhanced retrieval"
} | 98bb5c64-a312-41ec-af7d-8e9ffc3e1584 |
[
{
"from": "human",
"value": "How do I implement multi-hop RAG for questions that require chaining multiple retrieval steps? Give me a concrete example."
},
{
"from": "gpt",
"value": "Multi-hop RAG answers questions like \"What is the population of the country where the CEO of OpenAI was born?\" ... | {
"category": "multi_hop_rag",
"context": "multi-hop retrieval for complex questions"
} | 5dfdf96a-18e5-4f43-8da3-e76b989fcbaa |
[
{
"from": "human",
"value": "What are advanced RAG patterns like HyDE, RAG-Fusion, FLARE, and Self-RAG? Give me code examples for at least two of them."
},
{
"from": "gpt",
"value": "Advanced RAG patterns address the core weaknesses of naive retrieval: query-document mismatch, single-query blind... | {
"category": "rag_architecture",
"context": "advanced RAG patterns"
} | e15f7342-b226-4a45-88e2-1bebe5611c32 |
[
{
"from": "human",
"value": "How do I handle different document types — PDFs, HTML pages, code files, and tables — when ingesting data into a RAG system?"
},
{
"from": "gpt",
"value": "Different document types require specialised loaders and parsers. Using the wrong one loses structure (table ce... | {
"category": "document_ingestion",
"context": "handling PDF, HTML, code, and tables in RAG pipelines"
} | d1445812-f156-44c2-8308-f66cbbc6174f |
[
{
"from": "human",
"value": "My RAG system has poor answer quality. How do I diagnose whether the problem is low recall, hallucinations, or context stuffing, and fix each?"
},
{
"from": "gpt",
"value": "Poor RAG quality always traces back to one of three failure modes: the right document was nev... | {
"category": "retrieval_evaluation",
"context": "debugging RAG failures — low recall, hallucinations, context stuffing"
} | 2082a16e-2310-422b-b853-5d66e3748370 |
[
{
"from": "human",
"value": "How do I implement multi-hop RAG for questions that require chaining multiple retrieval steps? Give me a concrete example."
},
{
"from": "gpt",
"value": "Multi-hop RAG answers questions like \"What is the population of the country where the CEO of OpenAI was born?\" ... | {
"category": "multi_hop_rag",
"context": "multi-hop retrieval for complex questions"
} | 6775562c-6045-485b-95ce-b6c5dfbeae76 |
[
{
"from": "human",
"value": "Explain the main chunking strategies for RAG: fixed-size, semantic chunking, recursive character splitter, and parent-child chunks. When should I use each?"
},
{
"from": "gpt",
"value": "Chunking is one of the highest-leverage decisions in RAG. The wrong strategy cau... | {
"category": "chunking_strategies",
"context": "chunking strategies for RAG"
} | 35882648-6e4f-46c5-bf4e-909aab866f54 |
[
{
"from": "human",
"value": "How do I choose between text-embedding-3-small, BGE embeddings, sentence-transformers, and Cohere Embed for my RAG system?"
},
{
"from": "gpt",
"value": "Embedding model choice affects retrieval quality, latency, and cost more than any other single component. Here is... | {
"category": "embedding_models",
"context": "comparing embedding models for RAG"
} | d61f794e-fc10-4ed3-a19e-dae88c8cee6c |
[
{
"from": "human",
"value": "What are advanced RAG patterns like HyDE, RAG-Fusion, FLARE, and Self-RAG? Give me code examples for at least two of them."
},
{
"from": "gpt",
"value": "Advanced RAG patterns address the core weaknesses of naive retrieval: query-document mismatch, single-query blind... | {
"category": "rag_architecture",
"context": "advanced RAG patterns"
} | 3362036a-3ab8-4df4-956e-ecd6eb591c84 |
[
{
"from": "human",
"value": "How do I choose between text-embedding-3-small, BGE embeddings, sentence-transformers, and Cohere Embed for my RAG system?"
},
{
"from": "gpt",
"value": "Embedding model choice affects retrieval quality, latency, and cost more than any other single component. Here is... | {
"category": "embedding_models",
"context": "comparing embedding models for RAG"
} | 34a942d4-f3a9-4d2d-84a3-c261c3858e79 |
[
{
"from": "human",
"value": "How do I build a RAG system that generates responses with inline citations linking back to source documents?"
},
{
"from": "gpt",
"value": "Citation generation ensures every claim in the answer is traceable to a specific source, which is critical for legal, medical, ... | {
"category": "citation_generation",
"context": "generating cited responses linked to source documents"
} | b007b1e5-aa30-464a-bdfa-02d96b98c016 |
[
{
"from": "human",
"value": "Walk me through designing a basic RAG pipeline from scratch. What are the core components and how do they fit together?"
},
{
"from": "gpt",
"value": "A Retrieval-Augmented Generation (RAG) pipeline has five core stages: document loading, chunking, embedding, retriev... | {
"category": "rag_architecture",
"context": "basic RAG pipeline design"
} | d0ade982-9a48-463b-9c8c-41b43d102c73 |
[
{
"from": "human",
"value": "How do I handle different document types — PDFs, HTML pages, code files, and tables — when ingesting data into a RAG system?"
},
{
"from": "gpt",
"value": "Different document types require specialised loaders and parsers. Using the wrong one loses structure (table ce... | {
"category": "document_ingestion",
"context": "handling PDF, HTML, code, and tables in RAG pipelines"
} | 3b67e636-0341-40f8-bd97-77bd3806a973 |
[
{
"from": "human",
"value": "My RAG system has poor answer quality. How do I diagnose whether the problem is low recall, hallucinations, or context stuffing, and fix each?"
},
{
"from": "gpt",
"value": "Poor RAG quality always traces back to one of three failure modes: the right document was nev... | {
"category": "retrieval_evaluation",
"context": "debugging RAG failures — low recall, hallucinations, context stuffing"
} | 0d6dce25-4892-4c85-90b3-17db217bd0a9 |
[
{
"from": "human",
"value": "How do I choose between text-embedding-3-small, BGE embeddings, sentence-transformers, and Cohere Embed for my RAG system?"
},
{
"from": "gpt",
"value": "Embedding model choice affects retrieval quality, latency, and cost more than any other single component. Here is... | {
"category": "embedding_models",
"context": "comparing embedding models for RAG"
} | 129a1a55-1804-48fe-93f4-13ad6bd8d38a |
[
{
"from": "human",
"value": "Explain the main chunking strategies for RAG: fixed-size, semantic chunking, recursive character splitter, and parent-child chunks. When should I use each?"
},
{
"from": "gpt",
"value": "Chunking is one of the highest-leverage decisions in RAG. The wrong strategy cau... | {
"category": "chunking_strategies",
"context": "chunking strategies for RAG"
} | 0b0781b6-c284-46fe-bc83-45bf0f43107e |
[
{
"from": "human",
"value": "How does reranking improve RAG? Show me how to implement a reranking step using Cohere and FlashRank."
},
{
"from": "gpt",
"value": "Initial retrieval (BM25 or dense ANN) optimises for speed: it runs a single dot product against all vectors. Rerankers run a slower bu... | {
"category": "reranking",
"context": "cross-encoder reranking in RAG"
} | 4284c176-3e87-4bd5-8195-bd7ea3408467 |
[
{
"from": "human",
"value": "What is GraphRAG and how do knowledge graphs enhance retrieval compared to standard vector search?"
},
{
"from": "gpt",
"value": "GraphRAG combines vector similarity search with graph traversal, enabling retrieval of information that requires understanding entity rel... | {
"category": "rag_architecture",
"context": "GraphRAG and knowledge graph-enhanced retrieval"
} | 4546ef2d-24f0-435d-b37f-7bf4b9c54d11 |
[
{
"from": "human",
"value": "What is contextual compression in RAG and how do I use LLMChainExtractor and EmbeddingsFilter to reduce context window usage?"
},
{
"from": "gpt",
"value": "Contextual compression sits between retrieval and generation. Instead of passing entire chunks verbatim to the... | {
"category": "contextual_compression",
"context": "compressing retrieved context to reduce LLM costs"
} | 0dd3f545-27f9-4aa2-82c5-06706ec9f2cc |
[
{
"from": "human",
"value": "How do I optimize a RAG system for production — caching, latency, and cost at scale?"
},
{
"from": "gpt",
"value": "Production RAG at scale has three main cost centres: embedding API calls, vector search latency, and LLM generation tokens. Here is how to tackle each.... | {
"category": "rag_production",
"context": "RAG caching, latency optimization, and cost control"
} | eacf5a29-81fd-4785-8522-86090b46b411 |
[
{
"from": "human",
"value": "How does reranking improve RAG? Show me how to implement a reranking step using Cohere and FlashRank."
},
{
"from": "gpt",
"value": "Initial retrieval (BM25 or dense ANN) optimises for speed: it runs a single dot product against all vectors. Rerankers run a slower bu... | {
"category": "reranking",
"context": "cross-encoder reranking in RAG"
} | 1ba7a433-875a-4c0f-a4dc-9a2ea264312e |
[
{
"from": "human",
"value": "How do I build a RAG system that generates responses with inline citations linking back to source documents?"
},
{
"from": "gpt",
"value": "Citation generation ensures every claim in the answer is traceable to a specific source, which is critical for legal, medical, ... | {
"category": "citation_generation",
"context": "generating cited responses linked to source documents"
} | 37858acd-a152-4b19-8ef6-d024745423f2 |
[
{
"from": "human",
"value": "What is contextual compression in RAG and how do I use LLMChainExtractor and EmbeddingsFilter to reduce context window usage?"
},
{
"from": "gpt",
"value": "Contextual compression sits between retrieval and generation. Instead of passing entire chunks verbatim to the... | {
"category": "contextual_compression",
"context": "compressing retrieved context to reduce LLM costs"
} | c51f1623-cac9-41fe-aa51-0a5b07717881 |
[
{
"from": "human",
"value": "How does hybrid search work in RAG? Show me how to combine BM25 sparse retrieval with dense embeddings using Reciprocal Rank Fusion."
},
{
"from": "gpt",
"value": "Hybrid search combines keyword-based BM25 (sparse) with semantic embedding search (dense). Each method ... | {
"category": "hybrid_search",
"context": "combining BM25 sparse search with dense embeddings"
} | 4c90a429-5be1-4887-afe9-9ac185d62d40 |
[
{
"from": "human",
"value": "How do I evaluate my RAG system using RAGAS? Walk me through faithfulness, answer relevancy, and context recall metrics with code."
},
{
"from": "gpt",
"value": "RAGAS (Retrieval Augmented Generation Assessment) provides reference-free and reference-based metrics tha... | {
"category": "retrieval_evaluation",
"context": "RAGAS evaluation pipeline for RAG"
} | ecea7bd8-da4c-42b7-953a-0172f78f32b3 |
[
{
"from": "human",
"value": "How does reranking improve RAG? Show me how to implement a reranking step using Cohere and FlashRank."
},
{
"from": "gpt",
"value": "Initial retrieval (BM25 or dense ANN) optimises for speed: it runs a single dot product against all vectors. Rerankers run a slower bu... | {
"category": "reranking",
"context": "cross-encoder reranking in RAG"
} | 290476f3-baac-469e-a59d-7b61110a8bb6 |
[
{
"from": "human",
"value": "Walk me through designing a basic RAG pipeline from scratch. What are the core components and how do they fit together?"
},
{
"from": "gpt",
"value": "A Retrieval-Augmented Generation (RAG) pipeline has five core stages: document loading, chunking, embedding, retriev... | {
"category": "rag_architecture",
"context": "basic RAG pipeline design"
} | 52ccbd36-b82d-4cad-9e3f-8f2328ecf968 |
[
{
"from": "human",
"value": "How does hybrid search work in RAG? Show me how to combine BM25 sparse retrieval with dense embeddings using Reciprocal Rank Fusion."
},
{
"from": "gpt",
"value": "Hybrid search combines keyword-based BM25 (sparse) with semantic embedding search (dense). Each method ... | {
"category": "hybrid_search",
"context": "combining BM25 sparse search with dense embeddings"
} | 629a6ebe-77b7-4316-8e9c-5ee3211918a9 |
[
{
"from": "human",
"value": "How do I handle different document types — PDFs, HTML pages, code files, and tables — when ingesting data into a RAG system?"
},
{
"from": "gpt",
"value": "Different document types require specialised loaders and parsers. Using the wrong one loses structure (table ce... | {
"category": "document_ingestion",
"context": "handling PDF, HTML, code, and tables in RAG pipelines"
} | 8209219d-aa08-4fd4-aa03-9ece204acca3 |
[
{
"from": "human",
"value": "How do I handle different document types — PDFs, HTML pages, code files, and tables — when ingesting data into a RAG system?"
},
{
"from": "gpt",
"value": "Different document types require specialised loaders and parsers. Using the wrong one loses structure (table ce... | {
"category": "document_ingestion",
"context": "handling PDF, HTML, code, and tables in RAG pipelines"
} | 7686a34d-3fa8-4175-b15c-8740cf9e47de |
[
{
"from": "human",
"value": "How do I build a RAG system that generates responses with inline citations linking back to source documents?"
},
{
"from": "gpt",
"value": "Citation generation ensures every claim in the answer is traceable to a specific source, which is critical for legal, medical, ... | {
"category": "citation_generation",
"context": "generating cited responses linked to source documents"
} | 33e42767-e99d-45d9-8538-66dae2377e26 |
[
{
"from": "human",
"value": "My RAG system has poor answer quality. How do I diagnose whether the problem is low recall, hallucinations, or context stuffing, and fix each?"
},
{
"from": "gpt",
"value": "Poor RAG quality always traces back to one of three failure modes: the right document was nev... | {
"category": "retrieval_evaluation",
"context": "debugging RAG failures — low recall, hallucinations, context stuffing"
} | 64bd1239-a2fb-4855-89ab-a46baff1b718 |
[
{
"from": "human",
"value": "How do I implement multi-hop RAG for questions that require chaining multiple retrieval steps? Give me a concrete example."
},
{
"from": "gpt",
"value": "Multi-hop RAG answers questions like \"What is the population of the country where the CEO of OpenAI was born?\" ... | {
"category": "multi_hop_rag",
"context": "multi-hop retrieval for complex questions"
} | fe38929e-8dc3-4a91-95f3-a251d9126553 |
[
{
"from": "human",
"value": "Compare Pinecone, Weaviate, ChromaDB, and pgvector for RAG. How do I connect to each from Python?"
},
{
"from": "gpt",
"value": "Each vector database targets a different part of the spectrum from developer convenience to enterprise scale. Here is a practical comparis... | {
"category": "vector_databases",
"context": "choosing and using vector databases"
} | 48d36c0f-2a98-4956-aad2-368cb780b4c8 |
[
{
"from": "human",
"value": "Compare Pinecone, Weaviate, ChromaDB, and pgvector for RAG. How do I connect to each from Python?"
},
{
"from": "gpt",
"value": "Each vector database targets a different part of the spectrum from developer convenience to enterprise scale. Here is a practical comparis... | {
"category": "vector_databases",
"context": "choosing and using vector databases"
} | 2a90aeb9-7557-4879-a15d-4db6dd9678c8 |
[
{
"from": "human",
"value": "How do I build a RAG system that generates responses with inline citations linking back to source documents?"
},
{
"from": "gpt",
"value": "Citation generation ensures every claim in the answer is traceable to a specific source, which is critical for legal, medical, ... | {
"category": "citation_generation",
"context": "generating cited responses linked to source documents"
} | 8ff025da-2c5b-4315-b324-28bb65fa2537 |
[
{
"from": "human",
"value": "My RAG system has poor answer quality. How do I diagnose whether the problem is low recall, hallucinations, or context stuffing, and fix each?"
},
{
"from": "gpt",
"value": "Poor RAG quality always traces back to one of three failure modes: the right document was nev... | {
"category": "retrieval_evaluation",
"context": "debugging RAG failures — low recall, hallucinations, context stuffing"
} | 32560531-654f-4e30-8751-b01c944af6b9 |
[
{
"from": "human",
"value": "How do I optimize a RAG system for production — caching, latency, and cost at scale?"
},
{
"from": "gpt",
"value": "Production RAG at scale has three main cost centres: embedding API calls, vector search latency, and LLM generation tokens. Here is how to tackle each.... | {
"category": "rag_production",
"context": "RAG caching, latency optimization, and cost control"
} | 9e37e839-2015-48f1-bcd5-fe3d816c199b |
[
{
"from": "human",
"value": "What are advanced RAG patterns like HyDE, RAG-Fusion, FLARE, and Self-RAG? Give me code examples for at least two of them."
},
{
"from": "gpt",
"value": "Advanced RAG patterns address the core weaknesses of naive retrieval: query-document mismatch, single-query blind... | {
"category": "rag_architecture",
"context": "advanced RAG patterns"
} | 96680067-cbb3-4f93-b9b1-8df2eae72174 |
[
{
"from": "human",
"value": "How do I handle different document types — PDFs, HTML pages, code files, and tables — when ingesting data into a RAG system?"
},
{
"from": "gpt",
"value": "Different document types require specialised loaders and parsers. Using the wrong one loses structure (table ce... | {
"category": "document_ingestion",
"context": "handling PDF, HTML, code, and tables in RAG pipelines"
} | e2d1f4ab-90fb-4029-9607-64d6e07ccf0d |
[
{
"from": "human",
"value": "How do I build a RAG system that generates responses with inline citations linking back to source documents?"
},
{
"from": "gpt",
"value": "Citation generation ensures every claim in the answer is traceable to a specific source, which is critical for legal, medical, ... | {
"category": "citation_generation",
"context": "generating cited responses linked to source documents"
} | 243feb0c-05db-492d-9140-f71ed899eb8b |
[
{
"from": "human",
"value": "How does reranking improve RAG? Show me how to implement a reranking step using Cohere and FlashRank."
},
{
"from": "gpt",
"value": "Initial retrieval (BM25 or dense ANN) optimises for speed: it runs a single dot product against all vectors. Rerankers run a slower bu... | {
"category": "reranking",
"context": "cross-encoder reranking in RAG"
} | 44fef4e0-7402-4d9b-aadd-3d11efa66609 |
[
{
"from": "human",
"value": "How do I optimize a RAG system for production — caching, latency, and cost at scale?"
},
{
"from": "gpt",
"value": "Production RAG at scale has three main cost centres: embedding API calls, vector search latency, and LLM generation tokens. Here is how to tackle each.... | {
"category": "rag_production",
"context": "RAG caching, latency optimization, and cost control"
} | 054d4cc9-bfbe-488a-99e9-ed24ca852a81 |
[
{
"from": "human",
"value": "How do I handle different document types — PDFs, HTML pages, code files, and tables — when ingesting data into a RAG system?"
},
{
"from": "gpt",
"value": "Different document types require specialised loaders and parsers. Using the wrong one loses structure (table ce... | {
"category": "document_ingestion",
"context": "handling PDF, HTML, code, and tables in RAG pipelines"
} | f3b34d4e-430f-4be4-9e99-1c5480377cfa |
[
{
"from": "human",
"value": "How do I choose between text-embedding-3-small, BGE embeddings, sentence-transformers, and Cohere Embed for my RAG system?"
},
{
"from": "gpt",
"value": "Embedding model choice affects retrieval quality, latency, and cost more than any other single component. Here is... | {
"category": "embedding_models",
"context": "comparing embedding models for RAG"
} | f1c48fb8-0aa5-4748-8f5f-bcb81fe1ce7f |
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