Learning outcomes
- Diagnose the actual gap
- Choose an adaptation strategy
- Combine strategies intentionally
Mental model
Prompting changes instructions, RAG supplies external knowledge, fine-tuning changes learned behavior, and tools provide actions or authoritative computation.
Theory
Diagnose the failure first. Missing current or private knowledge points toward retrieval. Inconsistent style or stable task behavior may justify fine-tuning after prompts and evaluations. Calculations, live state, and side effects belong in tools. These strategies combine: a fine-tuned model can call tools over retrieved evidence.
Alternatives and trade-offs
Long context can replace retrieval for small corpora, deterministic code can replace model reasoning for known rules, and a better base model may outperform adaptation complexity.
Failure modes and misconceptions
Do not fine-tune facts that change often, use RAG to enforce tone, call a model for deterministic arithmetic, or choose from fashion rather than measured constraints.
Knowledge check
Which intervention best addresses a model that cannot access today's account balance?
Decision scenario
A support assistant uses prompting for tone, RAG for policy, a billing tool for live balances, and fine-tuning only if labeled evaluations show a persistent behavior gap.
Relationships
Context Engineering
Strategy selection starts by diagnosing context and behavior gaps.
enablesIngestion and Chunking
Selecting retrieval leads to building a governed ingestion pipeline.
relatedPrompt Structure
Prompting is one strategy in a broader adaptation decision.
Primary sources
- Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks - arXiv, verified 2026-07-16
- Model Optimization - OpenAI, verified 2026-07-16