RAG System Design
ProDesign retrieval-augmented generation systems for enhanced AI accuracy with custom knowledge bases
Template Fields
e.g., Internal knowledge base Q&A, Document analysis system
Describe how documents will be split (e.g., by paragraph, fixed tokens, semantic boundaries)
Response time, accuracy targets, concurrent users
Describe security, privacy, and access control needs
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Related Resources
Chunking Strategies for RAG: Fixed, Semantic, Recursive, and Parent-Document
Chunking is the single biggest quality lever in most RAG pipelines. This tutorial walks through fixed-size, semantic, recursive, and parent-document chunking on a hypothetical legal-research assistant — with diagnoses, fixes, and failure modes.
HyDE Retrieval: Generating Hypothetical Answers to Improve Vector Search
HyDE (Hypothetical Document Embeddings) asks the model to draft a fake answer first, then retrieves against that. This tutorial walks through why it helps, when it hurts, and how to tune it on a hypothetical medical-literature corpus.
Context Engineering: The 2026 Replacement for Prompt Engineering
How context engineering — the discipline of assembling what a model sees — replaced prompt engineering as the 2026 quality lever. Strategies, patterns, and trade-offs.