61 — Semantic Caching (Redis/GPTCache)
Reuse validated answers for sufficiently similar requests while preserving tenant, policy, freshness, and task boundaries.
Advanced RAG & Data Architecture
Connect agents to relevant, current, authorized knowledge. Design retrieval you can evaluate.
Which evidence should the model trust?
- Evaluate retrieval separately from generation.
- Preserve source permissions and provenance.
- Treat freshness and context selection as design decisions.
62 — Hybrid Search (Dense + Sparse)
Combine lexical precision with semantic recall, then fuse candidate rankings under one authorization and evaluation contract.
63 — Metadata Pre-Filtering
Restrict the searchable evidence set using trusted metadata before semantic ranking, while validating the store’s actual filtering behavior.
64 — Parent-Child Chunking
Retrieve precise child passages, then expand them into bounded parent context that preserves meaning, provenance, and access boundaries.
65 — Re-ranking (Cohere/Cross-Encoders)
Use a stronger query-document relevance model on a bounded shortlist before expensive generation consumes the evidence.
66 — Query Deconstruction / Expansion
Turn complex or underspecified questions into bounded retrieval plans while preserving the original intent and evidence requirements.
67 — Graph RAG / Knowledge Graphs
Combine entity relationships with source passages to answer connection-oriented questions that isolated similarity matches cannot resolve.
68 — Time-Weighted Vector Retrieval
Blend semantic relevance with task-appropriate freshness while distinguishing publication time, event time, and validity.
69 — Vector Database Scaling (Pinecone/Milvus)
Scale vector retrieval by separating storage, indexing, query capacity, tenancy, and ingestion while measuring recall under real constraints.
70 — Dynamic Prompt Assembly
Build each model request from versioned policy, authorized evidence, task state, and a deliberate token allocation.
71 — Context Window Optimization
Allocate finite model context to the evidence and state that matter, preserving constraints and enough capacity for a complete response.
72 — Self-RAG / Self-Reflection
Use bounded evidence critique to decide when retrieval or revision is necessary, while avoiding self-confidence as a substitute for verification.
73 — Cross-Lingual Retrieval
Retrieve evidence across languages using evaluated multilingual representations or translation paths while preserving source meaning and access scope.
74 — Hypothetical Document Embeddings (HyDE)
Generate a hypothetical relevant passage to improve retrieval alignment, then answer only from real retrieved evidence.
75 — Chunk Overlap Tuning
Tune shared text between neighboring chunks to preserve boundary evidence without filling retrieval results with duplicates.
76 — Multi-Aspect Indexing
Index different views of the same source so queries can match its content, purpose, entities, and structure without losing source identity.
77 — Real-time Vector Ingestion
Keep retrieval synchronized with changing source systems using versioned events, idempotent processing, and measurable visibility lag.
78 — Lost in the Middle Mitigation
Test and reduce position-sensitive evidence loss in long prompts through selection, ordering, local extraction, and explicit coverage checks.
79 — Embedding Model Fine-Tuning
Adapt the retrieval representation to domain relevance using trustworthy examples, hard negatives, and a controlled re-indexing migration.
80 — Token Pruning
Remove low-value prompt content under a measured quality constraint while preserving negation, identifiers, evidence, and instructions.