Multi-Stage Retrieval
Purpose
[stub: multi-stage-retrieval]
Metadata
| Author | Amit Singh |
| Scope | agentic-ai-engineering |
Local graph
Linked from 5 notes
13. Perplexity: Architecture Case Study
An external, engineering-blog-grounded analysis of Perplexity's real-time research-agent architecture -- live web retrieval instead of a static corpus, citation grounding as a hard output constraint, and answer synthesis under a tight latency budget.
9. Haystack Agents
Covers Haystack's pipeline-based approach to building agents on top of its retrieval and NLP component graph, aimed at production search and RAG use cases.
5. Retrieval Policies
The decision layer that sits in front of Part 05's retrieval mechanics — whether to retrieve at all, how much to pull for a given query, and from which knowledge source, what over- and under-retrieving each cost you, and how Agentic RAG relocates the whole policy into the model's own reasoning loop.
Agentic AI Engineering
A book-shaped table of contents for Agentic AI Engineering: where 'LLM application' becomes 'agent' — introduction to agentic AI, agent cognition, memory systems, planning & reasoning algorithms, tools & environment interaction, retrieval & knowledge systems, and context engineering. Book 2 of the AI Systems Engineering series.
8. LlamaIndex Workflows
Covers LlamaIndex's event-driven Workflows abstraction — steps wired by typed events instead of an explicit graph — and why the framework's RAG-first origins make it the natural home for retrieval-heavy agents.
Related notes
8. Agentic RAG
Covers RAG architectures where an agent decides when and what to retrieve, iteratively refining queries and evaluating retrieved evidence rather than retrieving once up front.
3. Chunking Strategies
Compares fixed-size, recursive, and semantic chunking strategies and how chunk boundary choices determine whether retrieved passages preserve the meaning needed to answer a query.
2. Embeddings
Explains how embedding models turn text into dense vectors and the dimensionality, cost, and quality tradeoffs an architect weighs when picking a commercial versus open-source embedding model.
9. GraphRAG
Explains how knowledge-graph-structured retrieval captures entity relationships and multi-hop reasoning that pure vector similarity search misses.