How Do I Build a High-Quality AI Assistant?

High-quality AI assistant design starts with a clearly scoped job, documentation prepared for retrieval, and retrieval-augmented generation (RAG) that requires citations. Add refusal/escalation rules for missing evidence, apply access controls and prompt-injection defenses, and use recurring evaluations on real questions to track regressions.

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TL;DR

Build a documentation-grounded AI assistant that uses RAG to cite your approved docs, follows “don’t guess” rules when evidence is missing, applies access controls to prevent data leaks, and improves through continuous evaluation on a golden set of real questions.
Micro-CTA: Draft your top 25 support questions and map them to docs.

What “High-Quality” Means For Doc-Grounded Support

A “high-quality” documentation assistant reliably does four things:

1. Answers only when it has evidence in your docs (and cites it).
2. Refuses or escalates when the docs don’t support an answer.
3. Prevents unsafe behavior (prompt injection, data leakage, overreach).
4. Improves measurably via evals and feedback loops.

Definition (for consistency): RAG injects retrieved documentation into the model prompt at runtime so answers can be grounded in your content rather than relying only on pre-trained knowledge.

Step 1: Define The Job, Scope, And Success Metrics

Start with one job. For support, the job is typically: “answer repetitive product questions accurately and escalate safely.”

Define:

• Top 10–25 question clusters: (install/setup, troubleshooting, billing, permissions, policies).
• Answer scope: what the assistant should answer vs. refuse vs. escalate.
• Targets (examples):
  • Containment rate (deflection)
  • Escalation rate for sensitive topics
  • “Cited-answer rate” for factual/technical questions
  • Negative feedback rate (thumbs down) and top failure topics

Guardrail rule: If it can’t cite supporting documentation, it should not “guess.”

Step 2: Prepare Documentation So Retrieval Can Succeed

Most accuracy failures are content failures, not model failures.

Make docs retrievable (not just readable):

• One canonical answer per topic. Retire duplicates and near-duplicates.
• Add clear headings, step-by-step procedures, and “If X, then Y” policy language.
• Add versioning and applicability: “Applies to vX.Y,” “Last updated,” and deprecation notes.
• Split or clearly label content types: troubleshooting vs billing vs policy vs security.

Conflict rule: When two docs disagree, pick one as canonical and either redirect or explicitly mark the other as historical/outdated.

Step 3: Configure Retrieval + “Don’t Guess” Answer Rules

RAG helps only if you enforce behavior:

• Answer-from-evidence: Use retrieved snippets as the basis of the response.
• Citations required: For technical claims, include citations so users can verify and you can debug.
• Fallback policy: If the evidence is missing, say so and offer next steps (ask a clarifying question, link to where to find info, or escalate).
• Stable formats: Prefer consistent templates (Steps → Notes → Escalation).

Step 4: Add Safety, Privacy, And Access Controls Early

Treat the assistant like an internet-facing system:

• Prompt injection resilience: Assume adversarial inputs and test “ignore previous instructions,” “reveal system prompt,” and “use hidden data” attempts.
• Data access scope: Prevent private/internal docs from being retrievable by public users.
• Least privilege for actions: If you later add tools (refunds, account changes), require confirmations and audit trails.

For risk framing and governance checklists, NIST AI RMF and the GenAI profile can be used to translate goals into testable requirements.

Step 5: Build A Golden Set And Run Evals Continuously

You don’t “finish” an assistant, you operate it.

Golden set (start small):

• Collect 50–200 real user questions with expected outcomes (answer + citation, refuse, or escalate).
• Add pass/fail checks:
  • Groundedness: did it rely on retrieved docs?
  • Correctness: is the answer consistent with canonical docs?
  • Policy compliance: did it avoid restricted guidance?
  • Helpfulness: did it provide next steps when refusing?

OpenAI recommends building evals to monitor prompt behavior and regressions, especially when changing models or prompts.

Step 6: Deploy Narrow, Monitor, And Iterate

Roll out in phases:

• Start with one surface (help center widget or internal support) and a limited doc set.
• Track:
  • Top “no-answer” questions (doc backlog)
  • Thumbs down reasons (if captured)
  • Citation coverage rate
  • Escalation rate by category (billing/security/account)

Then iterate:

• Fix the root cause (doc gap, conflicting docs, retrieval mismatch, unclear instruction).
• Add the fixed cases back into the golden set to prevent regressions.

How To Do It With CustomGPT.ai

Below is a concrete setup sequence mapped to CustomGPT.ai controls:

1. Create the agent from your documentation sources (website URL/sitemap).
2. Turn on citations so responses can point to the supporting source.
3. Use agent settings to define behavior (persona/setup instructions, and “I don’t know” messaging).
4. Harden against prompt injection and hallucinations using platform guidance and recommended defaults.
5. Restrict deployment domains so the agent only runs where you allow.
6. Set a conversation retention period aligned to your policies.
7. Monitor queries, conversations, and user feedback (thumbs up/down) to find gaps and failures.
8. Deploy via embed (e.g., iFrame embed) and expand surfaces only after metrics stabilize.

Precision note: If you enable general LLM knowledge (“My Data + LLM”), CustomGPT documentation warns this can increase hallucination risk and generate answers not based on your data. Use only if you accept that trade-off.

Common Mistakes

• Indexing duplicate/conflicting docs and expecting the model to “figure out the right one.”
• Letting the assistant answer without citations on technical/support claims.
• Shipping without a golden set, then discovering failures only through customer tickets.
• Mixing public + private docs without clear access controls and deployment restrictions.

Edge Cases To Plan For

• Version drift: docs updated, but older pages still indexed and retrieved.
• Policy questions: billing/security/permissions require stricter escalation.
• Ambiguous user context: the assistant needs clarifying questions to pick the right procedure.

Conclusion

Building a high-quality AI assistant means scoping the job, preparing retrievable docs, enforcing RAG with citations, and running continuous evals.

Next Step: CustomGPT.ai provides doc indexing, citations, and controls – use its 7-day free trial to validate.

FAQ

How Do I Keep The Assistant Answering Only From My Documentation?

Use CustomGPT’s recommended configuration patterns for minimizing hallucinations and keeping the agent grounded in your sources, and avoid enabling general LLM knowledge unless you explicitly want broader (but less precise) answers. CustomGPT’s docs describe the trade-offs and recommend settings that prioritize “My Data Only” behavior.

How Do I Add Citations So Users Can Verify Answers?

Enable citations in the agent’s citation settings and choose a display format (end-of-answer or numbered inline references). This improves user trust and makes debugging easier because you can see exactly which source powered the response.

What Should The Assistant Do When The Docs Don’t Contain The Answer?

Treat “I don’t know” as a correct outcome: state that the information isn’t present in the documentation, ask a clarifying question if appropriate, and provide the next step (escalate to support, open a ticket, or link to the closest relevant doc). Then log the question as a documentation backlog item.

How Do I Measure Whether Accuracy Is Improving Over Time?

Create a golden set of real questions with expected behavior and run evals whenever you change docs, prompts, or models. Track pass/fail rates for groundedness, correctness, and policy compliance so improvements are measurable and regressions are caught early.