When people say "we want a custom AI," they usually mean one of two things:
1. AI that answers questions using their documents
2. AI that behaves in a specific, consistent way for a task
These map to two approaches:
- RAG (Retrieval-Augmented Generation)
- Fine-tuning
Both are powerful — but they solve different problems. This guide breaks them down in plain language, with enough technical accuracy to make the right business decision.
What is RAG?
RAG = search + AI response.
A RAG system retrieves relevant content (your policies, contracts, playbooks, knowledge base, or documentation) and supplies it to the AI model as context. The model then generates an answer using that retrieved content.
Best for: "Answer questions using our content."
Common examples (legal + SaaS)
Legal
- "What does our MSA say about liability caps?"
- "Summarize the termination clause in this contract."
- "Do we have a policy for data retention?"
SaaS
- "How do we handle SSO setup for enterprise customers?"
- "What's the troubleshooting process for error X?"
- "Summarize the top issues from this customer thread."
Why RAG is so useful
RAG is often the fastest way to get "company-aware AI" because:
- you don't need to train a model
- updates are immediate — update the documents, not the AI
- it's easier to validate and audit (you can show sources)
What is fine-tuning?
Fine-tuning = training the model to respond in a specific way.
Instead of feeding the model your documents at runtime, you give it examples during training (prompt → response). Over time, the model learns patterns and can produce outputs more consistently.
Best for: "Do this task consistently and predictably."
Common examples (legal + SaaS)
Legal
- Classify contract risk level based on language
- Produce standardized clause summaries
- Generate structured extraction output for contract metadata
SaaS
- Categorize support tickets into standardized taxonomies
- Draft responses in your brand voice
- Extract structured fields from customer messages consistently
What fine-tuning is not
Fine-tuning is not the best way to make the model "know your documents." It can learn patterns and style, but it will not reliably contain your changing policies unless those are repeatedly included in training data — and that becomes hard to maintain.
The simplest way to choose
Here's a practical rule:
✅ If your model needs to use your knowledge, start with RAG
✅ If your model needs to behave consistently, consider fine-tuning
Most businesses should start with RAG because it's:
- faster to build
- easier to maintain
- easier to evaluate and audit
- lower risk
When RAG is the right choice
RAG is great when:
- you want answers grounded in internal docs
- your content changes frequently
- you need citations or references
- you want faster iteration without retraining
- you want to keep model behavior consistent without training
Example:
A legal team wants a "contract assistant" that:
- answers questions about internal playbooks
- references clauses in the contract
- cites the source documents used
That's a classic RAG system.
When RAG fails (and why)
RAG breaks down when:
- your documents are messy or contradictory
- retrieval quality is poor (wrong doc chunks)
- the task needs strict output formatting
- you need the model to "learn a pattern," not pull facts
- the workflow is primarily classification/automation, not Q&A
Common failure modes:
- retrieving irrelevant pages
- incomplete context causing wrong conclusions
- ambiguous policies producing inconsistent answers
Fix:
RAG success depends on document quality and retrieval design:
- clean source-of-truth docs
- good chunking strategy
- metadata filtering
- relevance evaluation
- fallback behavior when retrieval is weak
When fine-tuning is worth it
Fine-tuning is valuable when:
- you have hundreds or thousands of examples
- you need consistent structured outputs
- classification accuracy matters
- you want less prompt engineering
- the task is repetitive and standardized
- you can budget time for training cycles
Example:
A SaaS support team wants the AI to classify tickets into 20 categories with high accuracy and output JSON in a strict schema. Fine-tuning can help.
Cost and maintenance: RAG vs fine-tuning
Both approaches have ongoing maintenance — just in different places.
RAG maintenance
You maintain:
- the document store (what's indexed)
- retrieval quality (what gets pulled)
- chunking and embeddings
- metadata and filters
- evaluation and monitoring
The upside: Updating content is simple. No retraining needed.
Fine-tuning maintenance
You maintain:
- training data quality
- labeling consistency
- retraining cycles
- evaluation sets
- model versioning and rollback
The upside: Model behavior can become extremely consistent.
The best approach for most teams: RAG + lightweight rules
In real systems, the most successful approach is often:
RAG + light business rules + human review
Example: Legal contract workflow
1. Upload contract
2. Extract metadata (counterparty, term, dates)
3. Retrieve internal playbook and policy guidance
4. Summarize risks and highlight relevant clauses
5. Human approves the summary and next steps
Example: SaaS support workflow
1. Ticket comes in
2. Retrieve relevant KB articles and past resolutions
3. Draft response + categorize
4. Human approves and sends
Decision checklist (quick)
Choose RAG if:
- your content changes often
- you want answers grounded in internal docs
- you need citations
- you want faster iteration
Choose fine-tuning if:
- you need strict formatting (schemas)
- the task is repetitive classification
- you have strong training data
- consistency matters more than "knowledge retrieval"
What we recommend
For most legal and SaaS organizations:
- Start with RAG
- Add guardrails, confidence thresholds, and monitoring
- Use fine-tuning only when you can prove it improves outcomes
RAG gets you to value faster — and the architecture can later incorporate fine-tuning if needed.
Want help deciding which approach fits your workflows?
Stratus Logic can assess your use cases and design the right architecture — RAG, fine-tuning, or a blended approach.