fleet-memory/hindsight-docs/versioned_docs/version-0.4/developer/rag-vs-hindsight.md

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RAG vs Memory

Traditional RAG (Retrieval-Augmented Generation) retrieves documents similar to a query. Hindsight provides structured memory with temporal reasoning, entity understanding, and belief formation.

Capability Comparison

Capability	RAG	Hindsight
Search strategy	Semantic similarity only	Semantic + keyword + graph + temporal
Multi-hop reasoning	Limited to retrieved chunks	Graph traversal across entity relationships
Temporal queries	Keyword matching ("spring")	Date parsing and range filtering
Entity understanding	None	Entity resolution, co-occurrence tracking
Knowledge consolidation	Stateless	Mental models that synthesize and evolve
Disposition	None	3 traits (skepticism, literalism, empathy) influence interpretation

Architecture Comparison

RAG

Step	Operation
1	Embed query
2	Vector similarity search
3	Return top-k chunks
4	Generate response

Single retrieval strategy. No state between queries.

Hindsight

Step	Operation
1	Parse query (extract temporal expressions, entities)
2	Execute 4 parallel retrievals: semantic, BM25, graph, temporal
3	Fuse results with RRF
4	Rerank with cross-encoder
5	Apply disposition traits
6	Generate response

Multiple retrieval strategies. Persistent state across sessions.

Example Scenarios

Multi-Hop Reasoning

Stored facts:

• "Alice is the tech lead on Project Atlas"
• "Project Atlas uses Kubernetes"
• "Kubernetes cluster had an outage Tuesday"

Query: "Was Alice affected by recent issues?"

System	Result
RAG	Retrieves facts about Alice only (no semantic similarity to "issues")
Hindsight	Traverses Alice → Project Atlas → Kubernetes → outage via entity links

Temporal Queries

Stored facts with timestamps:

• March: "Alice started microservices migration"
• April: "Alice completed auth service"
• October: "Alice focusing on performance"

Query: "What did Alice do last spring?"

System	Result
RAG	Returns all Alice facts regardless of date
Hindsight	Parses "last spring" → March-May, filters to that range

Entity Understanding

Stored facts about a user across sessions:

• "Pro subscription"
• "Mobile app crashes in settings"
• "Switched to annual billing"
• "Desktop app working fine"

Query: "What do you know about my account?"

System	Result
RAG	Lists disconnected facts
Hindsight	Returns connected facts via entity graph: subscription status, billing, known issues

Knowledge Evolution

Week 1: User struggles with async Python, succeeds with threads Week 3: User asks about asyncio, implements async database calls

System	Behavior
RAG	No memory of progression
Hindsight	Consolidates mental model "user prefers sync" → refines to "user growing comfortable with async"

When to Use Each

Use Case	Recommended
Document Q&A over static corpus	RAG
Search with no temporal requirements	RAG
AI assistants with persistent memory	Hindsight
Applications requiring entity tracking	Hindsight
Systems needing consistent disposition	Hindsight
Temporal queries ("last month", "in 2023")	Hindsight