How To Assess A Document Chat Use Case

Use this guide when a client or internal team approaches you with a concrete request, such as "we need users to chat with 1,000 documents," and wants architectural help. Treat the request as an input, not the architecture. Your job is to turn the request into a measurable, governed, Microsoft-aligned recommendation.

If the request is still only an idea, start with how to select and prioritize use cases. If the work is already approved as a full engagement, use how to run an agentic AI engagement.

1. Confirm The Actual Work

Start by separating the requested interface from the business work it must improve.

Ask the requester:

• Who will use the chat experience?
• What decisions, tasks, or work products should it support?
• Which questions do users need answered from the documents?
• What happens today when users need those answers?
• What KPI should improve: time to answer, handling time, accuracy, compliance review effort, onboarding time, support deflection, or another measure?
• What would make the pilot a scale, redesign, pause, or stop decision?

Capture the request in the use-case inventory. Use the request wording as the initial use-case name, then add the business outcome and KPI before discussing platform choices.

Exit when the requester can state the target users, workflow, business owner, KPI baseline, and desired pilot result.

2. Decide Whether This Is An Agent

Most "chat with documents" requests start as knowledge retrieval, not an agentic workflow. Do not design a custom agent until the need requires reasoning, planning, tool use, or adaptive task execution.

Use this routing:

Finding	Route
Users only need answers, summaries, comparisons, or citations from approved documents	Route to search/RAG or Microsoft 365-grounded productivity.
Users need the system to draft a work product using the documents	Route to grounded content generation with review.
Users need the system to read documents and update a system of record	Route to an agent or workflow with tool access, approvals, and audit.
Users need repeatable extraction, classification, or reporting from documents	Route to document processing, analytics, or workflow automation.
Users need decisions that are regulated, financial, contractual, or externally visible	Add human approval, deterministic workflow boundaries, and stronger evidence.

Record the classification and any "not an agent" rationale in the use-case inventory. A pure document Q&A pilot can still be valuable even when the right answer is search/RAG rather than an agent.

3. Inventory The Corpus

Create a document data map before selecting Microsoft 365 Copilot, Copilot Studio, Foundry, Azure AI Search, or a custom approach.

For the 1,000-document corpus, capture:

Area	Questions
Location	Are the documents in SharePoint, OneDrive, Teams, a file share, a document management system, a CRM, an external SaaS platform, or local exports?
Authority	Which repository is the system of record? Who owns document accuracy and retirement?
Format	Are the documents Word, PDF, PowerPoint, email, scanned images, HTML, markdown, spreadsheets, or mixed formats?
Quality	Are documents current, complete, deduplicated, searchable, and named consistently?
Metadata	Which metadata is needed for filtering, access, citations, lifecycle, jurisdiction, product, customer, or version?
Permissions	Do document permissions already reflect who may see each answer? Are there group, role, customer, matter, or project boundaries?
Sensitivity	Do the documents contain personal data, confidential data, regulated records, trade secrets, or customer-specific data?
Freshness	How often do documents change, and how quickly must changes appear in answers?
Retention	Which retention, legal hold, deletion, residency, or archival rules apply?

Fill the data readiness assessment for each source or source group. Do not treat document count as the main complexity driver. Source authority, permissions, quality, metadata, and update behavior matter more than "1,000 documents."

Exit when authoritative sources, owners, permissions, freshness, metadata, and compliance constraints are documented.

4. Define The Answer Contract

Agree what a correct chat response must look like before evaluating platforms.

Define:

• Whether answers must cite source documents and page, section, paragraph, or URL.
• Whether the system may synthesize across multiple documents.
• Whether the system must say "I do not know" when sources are missing or conflicting.
• Whether users can ask follow-up questions across the same session.
• Whether the experience needs filters such as product, region, client, policy version, or date range.
• Whether users can upload ad hoc documents.
• Whether responses can include generated drafts, tables, checklists, or comparison matrices.
• Whether conversations or prompts are retained, audited, or exported.

Use these rules as acceptance criteria in the pilot validation plan.

Exit when the answer style, citation requirement, refusal behavior, filters, retention, and review expectations are explicit.

5. Choose The Grounding Pattern

Use the retrieval decision register to document how the experience should access knowledge and actions.

Situation	Recommended Starting Pattern	Escalate When
Documents already live in SharePoint, OneDrive, or Teams with usable permissions	Microsoft 365 Copilot, Microsoft Search, Copilot connectors, or a Microsoft 365 Copilot agent	The experience needs custom workflow, custom UI, advanced evaluation, non-Microsoft sources, or stronger retrieval control.
Documents live in approved enterprise repositories outside Microsoft 365	Microsoft Graph connector or approved connector feeding Microsoft 365 search or Copilot	The source needs custom ingestion, metadata shaping, chunking, enrichment, or ranking control.
The team needs a business-user configurable chat experience with knowledge and limited actions	Copilot Studio with approved knowledge sources, connectors, and Power Platform governance	The pilot needs pro-code orchestration, custom evaluation, model control, or complex integration.
The team needs custom retrieval, advanced evaluation, model selection, or a custom app	Microsoft Foundry with Azure AI Search and governed Azure services	The architecture needs special runtime isolation, custom infrastructure, or nonstandard orchestration.
The request includes writeback, approvals, or transactions	Agent or deterministic workflow with narrow tools, least privilege, approval, rollback, and audit	Autonomous action would create unacceptable residual risk.

Prefer the lowest-complexity Microsoft-aligned pattern that can satisfy the answer contract and control requirements. For a simple "chat with 1,000 documents" request, the first recommendation is often Microsoft 365-grounded search or RAG, not a custom agent.

Exit when the selected pattern, rejected alternatives, assumptions, and control requirements are recorded.

6. Assess Security, Compliance, And Operations

Review controls before the pilot is built.

Minimum checks:

• Permission trimming prevents users from receiving answers from documents they cannot access.
• Sensitive labels, DLP, retention, residency, and audit requirements are understood.
• Prompt injection risks from document content are considered.
• The system can show source evidence for answers.
• Conversation and retrieval logs have an owner, retention rule, and access boundary.
• The pilot has cost, latency, availability, support, and incident expectations.
• There is a pause or rollback path if answers are unsafe, stale, or overexposed.

Record risks and controls in the governance risk control register. If the solution becomes an agent, create an agent charter and add the agent to the agent registry.

Exit when the business owner, data owner, security, compliance, platform, and operations stakeholders agree the pilot can be tested within acceptable boundaries.

7. Design A Narrow Pilot

Do not pilot against the full corpus unless the full corpus is clean, permissioned, and operationally ready. Pick a representative slice that can prove the riskiest assumptions.

Recommended pilot scope:

• One to three user groups.
• One document domain or business process.
• A controlled source set with known owners and permissions.
• Representative question types, including easy, hard, ambiguous, conflicting, and out-of-scope questions.
• A golden test set with expected answers, required citations, and pass/fail criteria.
• A clear path for user feedback, defect triage, and source remediation.

Measure:

Area	Example Metric
Business value	Time saved, case handling time, support deflection, review effort, onboarding speed.
Answer quality	Correctness, completeness, relevance, citation quality, unsupported-claim rate.
Grounding	Source coverage, freshness, permission trimming, conflicting-source handling.
Safety and compliance	Data leakage tests, prompt injection tests, sensitive-data handling, audit completeness.
Operations	Latency, failure rate, cost, support tickets, source update turnaround.
Adoption	Active users, repeated use, task completion, user satisfaction, fallback rate.

Exit when the pilot can produce a scale, redesign, pause, or stop decision backed by evidence.

8. Make The Architecture Recommendation

Present the recommendation as a decision, not a demo plan.

Include:

• The business outcome and KPI the chat experience is expected to improve.
• The routing decision: search/RAG, Microsoft 365 extension, Copilot Studio agent, Foundry/custom agent, workflow, analytics, stop, or defer.
• The selected Microsoft pattern and why simpler or more complex options were rejected.
• The data readiness findings and remediation required before build.
• The grounding pattern, permission model, and citation requirement.
• The required governance, security, compliance, and operations controls.
• The pilot scope, success thresholds, and decision gate.
• The artifact list and owners.

Use the agent lifecycle roadmap to confirm the recommendation is aligned with the broader adoption path.

Exit when decision owners can approve one of four actions: proceed to pilot, remediate data first, redesign the request, or stop.

Definition Of Done

The assessment is complete when the requester and decision owners have:

• A use-case inventory row with owner, users, workflow, KPI, classification, and routing decision.
• A data readiness assessment for the document sources.
• A retrieval decision register entry for the grounding pattern.
• An answer contract with citations, refusal behavior, filters, and retention expectations.
• A governance risk control register entry for material risks.
• A pilot validation plan with test questions, expected answers, metrics, thresholds, and owners.
• An architecture recommendation that explains whether the request needs an agent, search/RAG, workflow, analytics, an existing Microsoft capability, or no build.