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 |