Memory, RAG, CAG, and Fine-Tuning

Memory, RAG, CAG, and Fine-Tuning

Fact-check status

Reviewed claim by claim on 2026-09-02 against the primary or official sources identified inline as S1 and S2. Recommendations are design guidance unless a cited standard makes them normative.

Key jargon

Term Plain-language meaning
Working memory Short-lived state used during the current task or conversation. S1
Long-term memory Selected information persisted for future interactions. S1
Cache-augmented generation (CAG) Reusing preloaded or cached context rather than retrieving it for every request. S1
Fine-tuning Updating model parameters using additional training examples. S1

Key concepts

Concept map

flowchart LR
    A["Classify information need"] --> B["Choose state memory retrieval cache or tuning"]
    B --> C["Apply retention and trust rules"]
    C --> D["Evaluate behavior"]

Diagram semantics verified against S1 and S2 on 2026-09-02.

Need Prefer S1
Current authoritative facts Retrieval/RAG S1
Small stable reference corpus reused frequently Cached context/CAG where supported S1
User preference or workflow state Explicit application memory/state store S1
Consistent style, format, or task behavior Prompting first; fine-tuning if evals justify it S1
New domain vocabulary or behavior at scale Task-specific fine-tuning with held-out evals S1
Deterministic business rule Code/policy engine S1

Fine-tuning does not provide reliable record-level lookup, easy deletion, current citations, or permission filtering. RAG does not change the model’s underlying behavior. Memory should not silently turn every conversation into permanent user profiling. S1

Exercise

Classify ten requirements as prompt, retrieval, memory, fine-tuning, or deterministic code. Defend each choice using freshness, privacy, update, and evaluation needs.