How to Build an AI Research Assistant (Playbook)

Research is where language models are most tempting and most dangerous: they produce fluent, well-structured answers with citations that sometimes do not exist. An AI research assistant that professionals can rely on is built around one discipline: every claim traces to a verified source passage, and the assistant says what it could not find. This playbook covers building one, following FISTA's AI agents practice and the retrieval architecture in the enterprise RAG reference architecture whitepaper. Context on the function is in ai for research teams.

What does a research assistant do?

Stage	Function
Understand	Clarify the question, scope, timeframe, and output format
Plan	Decompose into sub-questions and choose sources
Retrieve	Execute queries across sources, iterate on gaps
Verify	Check every candidate citation supports its claim
Synthesize	Structured findings with provenance and explicit gaps
Deliver	Report for analyst review, with source list and audit trail

Step 1: Define tasks and sources

Specify the research task types in scope (market briefs, technical comparisons, regulatory summaries, literature reviews, due-diligence sections) and the output formats. Define the approved sources: internal repositories with permission mapping, licensed databases, and any external web access constrained to allowlisted domains. Define prohibited behavior: no claims without citations, no access outside approved sources, no conclusions on regulated matters presented as advice. See how to write an ai spec.

Step 2: Build the retrieval foundation

Index internal sources with structure-preserving ingestion, metadata, and permission filtering; enable hybrid retrieval with reranking. Build connectors to licensed and allowlisted external sources that return passages with stable identifiers and capture retrieval provenance (source, URL or record ID, retrieval time). Foundations are in how to build a rag system and how to build a hybrid search system.

Step 3: Implement query planning and multi-step retrieval

Single-shot search fails on real research questions. Implement a loop:

1. Decompose the question into sub-questions.
2. For each, generate queries per source, rewritten for that source's conventions.
3. Retrieve and rerank; extract candidate findings with passages.
4. Identify gaps and contradictions; generate follow-up queries.
5. Stop on coverage criteria, budget, or iteration limit.

Bound iterations, tool calls, cost, and time per task; research loops run away without limits. The agentic pattern is in how to build an agentic rag system and what is query rewriting.

Step 4: Implement citation verification

Verification is a hard check outside the model:

• Every claim in the synthesis carries a citation identifier.
• The identifier must resolve to a retrieved passage in this task's trace.
• An entailment or quote-match check confirms the passage supports the claim; unsupported claims are flagged or removed.
• Citations to sources not in the retrieval trace are blocked.
• The final report shows the cited passage on hover or inline.

This step converts fabrication from a risk into a detectable failure. Concepts are in what is groundedness in ai and llm output validation.

Step 5: Synthesize with provenance and gaps

Synthesis produces structured output per the task's format: findings grouped by sub-question, each with citations; contradictions between sources stated explicitly; confidence indicated; and a gaps section listing what was searched for and not found. The gaps section is what distinguishes a research tool from a text generator. For high-consequence tasks, add a critic pass that checks the synthesis against the citations before delivery; see the multi-agent orchestration patterns whitepaper.

Step 6: Deliver for analyst review

The analyst receives the report, the source list, and the trace of what was searched. The interface supports drilling into any citation, requesting deeper retrieval on a sub-question, and marking findings as verified, disputed, or irrelevant. Those marks feed evaluation. The analyst remains the author of any conclusions.

Step 7: Evaluate accuracy, coverage, and citations

Build a set of real research questions with expert-written reference answers listing key points and authoritative sources. Measure:

Metric	Method
Factual accuracy	Expert grading of claims
Coverage	Share of reference key points found
Citation validity	Programmatic: every citation resolves and supports
Gap correctness	Correctly reports absence for unanswerable sub-questions
Fabrication resistance	Adversarial questions with no available answer
Cost and time per task	Traces

Calibrate any model grader against expert labels. Wire the suite into CI. Method is in the AI evaluation and testing whitepaper.

Step 8: Controls and operations

• Permission-aware retrieval so analysts see only authorized internal content.
• Allowlisted external access with provenance capture.
• Budgets per task; alerts on runaway loops.
• Full trajectory logging for audit and debugging; see the AI observability whitepaper.
• Injection defenses for retrieved external content; see prompt injection defense checklist.

What does it cost to run?

Research tasks are among the more expensive agent workloads because they involve many retrieval and model calls per task. Cost is controlled by iteration limits, reranker cutoffs, model routing for sub-steps, and caching of repeated sub-questions. Measure cost per completed task against analyst hours saved. Drivers are in cost of running llms in production.

What are the common mistakes?

• Allowing open web access without allowlists or provenance.
• Treating citation instructions in the prompt as verification.
• Synthesis that fills gaps instead of reporting them.
• No iteration or cost limits.
• Evaluating fluency instead of coverage and accuracy.
• Jumping to multi-agent architectures before a single planned-retrieval agent is measured.

How do you handle conflicting sources?

Sources disagree, and an assistant that picks one silently misleads. Surface the disagreement, cite both, note dates and authority where known, and let the researcher decide. Evaluation cases should include known conflicts so the behavior is tested rather than assumed.

How FISTA Solutions builds research assistants

FISTA Solutions builds research assistants to this playbook: source-scoped specifications, permission-aware hybrid retrieval, planned multi-step retrieval with budgets, hard citation verification, synthesis with explicit gaps, expert-labeled evaluation in CI, and full trajectory logging. The AI agents practice delivers the assistant, AI enablement the retrieval and gateway platform, and forward deployed engineers embed with your analysts to define tasks, sources, and reference answers. The record behind the work is 150+ projects with 99.9% uptime.

To scope a research assistant for your team, message FISTA on WhatsApp, or read ai due diligence for a domain-specific application.