Providing Effective Product Context to AI Models

Overview

One of the most frequent complaints from product managers when they first start using AI seriously for work tasks is: "The AI doesn't know anything about my product." This is true — and it is also completely solvable. An LLM has no knowledge of your product, your company, your users, your market position, your technical constraints, or your business goals unless you explicitly provide that information. The quality of the context you provide is, in most cases, the primary driver of whether AI output is generic and useless or specific and actionable.

Providing effective product context is not the same as dumping everything you know about your product into a prompt. More context is not always better context. What the AI needs is the right context, structured in the right way, at the right level of specificity for the task at hand. Over-providing context dilutes the signal; under-providing it forces the model to make assumptions that may or may not align with your reality. Developing judgment about what to include and how to structure it is the core skill this topic develops.

This topic covers four dimensions of product context provision: what types of context to include and how to choose among them, how to structure requirements and user stories so they serve as effective AI context, how to handle qualitative data like customer feedback and interview transcripts, and when to provide full documents versus pre-processed summaries. Each section includes a practical framework for making the right call quickly and confidently, along with ready-to-use prompt examples you can adapt to your own product work immediately.

The fundamental principle running through all of these sections is that context for AI should be purpose-built, not repurposed from documents written for humans. A Confluence page written for onboarding new team members contains a lot of context that is irrelevant — or actively confusing — when used as AI input for a requirements task. Building the habit of crafting context specifically for the AI task at hand is the transition from using AI as a chatbot to using it as a precision product instrument.

What Context to Include — Strategy Docs, User Research, Metrics, Competitive Data, Technical Constraints

Not all context is equal. For any given product task, some information is absolutely necessary for the AI to produce a useful output, some information is helpful but optional, and some information is noise that dilutes the analysis. Learning to distinguish between these three categories — and making the distinction deliberately rather than by instinct — is the first discipline of effective context provision.

The context hierarchy for product work:

Tier 1 — AI absolutely needs this: The specific goal the task is in service of (OKR, sprint goal, business outcome), the user segment being addressed (persona, segment name, key behavioral characteristics), any hard constraints that must be respected (technical, legal, budget, timeline, already-committed decisions), and the current state the proposed change is operating against. Without Tier 1 context, the AI is operating in a vacuum and will produce generic output.

Tier 2 — Helps the AI produce more targeted output: Historical performance data relevant to the task (conversion rates, retention data, usage metrics), competitive positioning (where your product stands vs. key competitors for the feature area in question), qualitative user research that supports or challenges the task direction, and stakeholder preferences or political constraints that affect feasibility. Tier 2 context improves specificity without being strictly necessary.

Tier 3 — Likely noise: Company history and background, detailed technical architecture documentation (unless the task is directly technical), unrelated features or product areas, and general market research that isn't specifically relevant to the task question. Tier 3 context consumes tokens without improving the output and often dilutes it.

Building a context brief for each class of PM task: A context brief is a purpose-built document (or template) that captures exactly the Tier 1 and Tier 2 context needed for a specific type of task. Rather than building context from scratch for every AI session, you maintain a set of context briefs — one for discovery work, one for sprint planning, one for stakeholder communication — and update them regularly. When you start a new AI session for a given task type, you paste the relevant context brief as your opening context block.

A context brief for a discovery task looks different from one for a sprint planning task, which looks different from one for a stakeholder communication task. The sections below cover the context requirements for each major class of PM work. Building context briefs for each class is the hands-on exercise at the end of this section.

Context types and their typical token budgets:
- Product vision + OKR: 100–200 tokens
- User segment description: 100–150 tokens per segment
- Current state behavior: 50–100 tokens
- Key metrics with definitions: 100–200 tokens
- Competitive positioning: 150–250 tokens
- Technical constraints: 50–150 tokens
- Stakeholder constraints: 50–100 tokens
- Acceptance criteria or definition of done: 50–100 tokens

A well-constructed context brief for most PM tasks should fit within 600–900 tokens — small enough to leave ample room for the task-specific content and output in the model's context window.

Hands-On Steps

Choose one of your current product initiatives. List every piece of information you believe the AI would need to help you with this initiative meaningfully.
Categorize each item as Tier 1 (necessary), Tier 2 (helpful), or Tier 3 (noise). Remove all Tier 3 items from consideration.
For each Tier 1 item, write it in 1–2 sentences of plain language. Replace all internal jargon and acronyms with definitions.
Estimate the token count for your Tier 1 context. If it exceeds 500 tokens, look for compression opportunities — combine items, remove redundant language.
Add the highest-value Tier 2 items until your total context brief reaches 600–900 tokens.
Test the context brief: paste it as the opening context block for three different task types (e.g., write a user story, prioritize two features, draft a stakeholder update). Evaluate whether the outputs reflect your actual product situation.
Refine the context brief based on what was missing from outputs or what was irrelevant. Create a final version you will use for this initiative going forward.

Prompt Examples

Prompt (context brief test):

[Context Brief — Payments Feature Initiative]
Product: ProjectFlow — B2B project management SaaS for architecture and engineering firms
Current initiative: In-app payments for milestone-based client billing
Q3 OKR: Enable $500K ARR from new SMB architecture firm accounts by end of Q3
Target user: Principal architects at firms with 5–30 employees who currently invoice via spreadsheet + email
Current state: Zero billing capability in product; all invoicing is done outside the platform
Key constraint: Payment processing must comply with PCI-DSS; engineering team cannot take on infrastructure changes this quarter — we must use a third-party payment processor (Stripe is approved)
Competitive context: FreshBooks and QuickBooks Online dominate invoicing for this segment; our differentiator is project-linked milestone billing (not generic invoice creation)
Stakeholder constraint: Sales team has committed this feature to 3 enterprise prospects for Q3 demos

Task: Using the context brief above, identify the top 3 risks to successfully delivering this initiative in Q3. For each risk, write: Risk statement | Likelihood (High/Med/Low) | Impact (High/Med/Low) | Proposed mitigation.

Output format: Markdown table.

Expected output: A risk table with three specific, context-grounded risks — not generic "technical complexity" or "scope creep" statements, but risks directly tied to the constraints, timeline, and competitive context provided.

Learning Tip: Maintain a "living context brief" document for each active product initiative. Keep it in a note or Notion page. Update it every sprint with the latest OKR status, new constraints, and any stakeholder changes. Starting every AI session for that initiative with this brief as your opening context block is the highest-leverage habit for consistent AI output quality across multiple sessions.

How to Structure Requirements and User Stories as AI Context

User stories and requirements are a common input to AI tasks — you feed them to AI to generate acceptance criteria, check for completeness, identify edge cases, estimate complexity, or generate related stories for adjacent scenarios. But the raw format that most teams store stories in (Jira descriptions, Confluence pages, or sticky notes) is poorly optimized for AI processing. Structuring your stories to serve as effective AI context takes an extra 5 minutes per story and pays significant returns in output quality.

The format that works best for AI consumption is an explicitly structured story that makes implicit product knowledge visible. The standard "As a [user], I want [capability] so that [benefit]" format is a good start — it is structured and semantically predictable. But it typically leaves out critical context that a human PM would fill in from background knowledge: the current state the user is operating in, the constraints the solution must respect, the definition of success for this story, and any edge cases that are explicitly in or out of scope.

The AI-optimized user story format:

User: [Role — include key behavioral characteristics that matter for this story]
User story: As a [user], I want [capability] so that [outcome]
Current state: [What the user does today without this capability; be specific]
Constraints: [Technical, business, or experience constraints the solution must respect]
Success definition: [What "done" looks like in measurable terms]
Explicitly out of scope: [What this story intentionally does NOT address]

This format serves AI tasks because it makes the implicit explicit. An experienced PM reading a user story brings enormous background knowledge to it — they know the product, the user's current workflow, the technical constraints, and the organizational context. The AI has none of that without being told. The "Current state" field alone dramatically improves acceptance criteria quality because it gives the AI a concrete baseline to write against.

How to include acceptance criteria without bloating the context: Acceptance criteria written as prose paragraphs are inefficient for AI processing. Reformat them as a numbered list with explicit structure: "Given [precondition], When [user action], Then [expected result]." This format is compact, unambiguous, and easy for the model to process, extend, or evaluate against. Avoid: "The system should allow users to download their reports in multiple formats, including PDF and CSV, and the file should be generated within 5 seconds." Prefer: "Given a report has been generated, When the user selects Download → PDF, Then the file downloads within 5 seconds and is formatted for A4 paper."

Structuring story sets as context: When you are feeding multiple related stories to AI (e.g., asking for gap analysis or sprint readiness review), format them consistently and number them. Add a "Relationship:" field to each story explaining how it relates to adjacent stories. This prevents the model from treating each story as isolated and enables it to identify cross-story dependencies, gaps, and conflicts.

Hands-On Steps

Pull 3 user stories from your current backlog. Read each one and identify: What implicit PM knowledge is required to understand this story that isn't written down?
For each story, rewrite it using the AI-optimized format: User / User story / Current state / Constraints / Success definition / Explicitly out of scope. Write down everything that was implicit before.
Count the token increase: how many more tokens does the enriched story take? For most stories, you will add 50–100 tokens — a worthwhile trade given the output quality improvement.
Feed both the original and enriched versions to AI with the same task: "Generate 4 Given/When/Then acceptance criteria for this story." Compare the specificity and testability of the outputs.
Identify three cases in the enriched story where the AI's acceptance criteria referenced the "Current state" or "Constraints" fields directly. These are the specific quality improvements that justify the enrichment effort.
Update your team's user story template in Jira, Confluence, or Linear to include the enriched fields. Write a 3-sentence rationale you can use to explain the change to your team.

Prompt Examples

Prompt (enriched story → acceptance criteria):

You are a senior business analyst. Using the structured user story below, generate 5 precise, testable acceptance criteria. Format each as Given/When/Then. After the criteria, add a "Coverage check" section: list 2 edge cases this story should address that are not yet covered by the 5 criteria.

User: Construction project manager at a general contractor firm; manages 3–8 active projects simultaneously; currently coordinates via email and spreadsheets
User story: As a project manager, I want to assign tasks to subcontractors from within the project plan so that I do not have to send assignment notifications separately via email
Current state: PM creates tasks in the platform but must copy task details into a separate email to notify subcontractors. Subcontractors do not have platform accounts.
Constraints: Subcontractors must not require a paid account to receive and acknowledge task assignments. Email must be the delivery channel (no SMS/push for this story). Assignment confirmation must be logged in the platform.
Success definition: PM can complete a task assignment without leaving the platform; subcontractor receives a notification email and can confirm acceptance via a link (no login required); confirmation status is visible to PM in the task view.
Explicitly out of scope: This story does not cover subcontractor ability to update task status, only to accept or decline.

Expected output: Five precisely scoped Given/When/Then acceptance criteria that reference the email-based assignment flow, the no-login subcontractor requirement, and the in-platform confirmation logging — plus two edge cases the PM should consider (e.g., what if the subcontractor email bounces? what if the same task is assigned to multiple subcontractors?).

Prompt (story set gap analysis):

You are a senior business analyst reviewing a story set for sprint readiness.

Below are 4 user stories for the "Subcontractor notification" feature. For each story, a "Relationship" field explains how it connects to adjacent stories.

Review the set and identify:
1. Any gaps — user scenarios that appear necessary but have no story covering them
2. Any dependencies — stories where one must be done before another (flag if not in the right sequence)
3. Any contradictions — acceptance criteria or constraints that conflict across stories
4. Stories that are too large for a single sprint (estimate based on described scope)

[Paste 4 enriched user stories here]

Output format: Use four headers — "Gaps", "Dependencies", "Contradictions", "Oversized stories" — with bullet points under each.

Expected output: A structured readiness review identifying specific gaps in the story set, sequencing issues, cross-story contradictions, and sizing concerns — the kind of analysis an experienced BA would provide in a refinement session, generated in under 2 minutes.

Learning Tip: The "Explicitly out of scope" field in your user story is one of the most valuable additions you can make for AI tasks. It does two things: it prevents the AI from generating acceptance criteria that go beyond the story's intent, and it forces you as the PM to think clearly about scope boundaries before writing the story. Many sprint scope creep situations start with stories that have no explicit out-of-scope boundary.

How to Include Customer Feedback, Interview Transcripts, and Survey Data Effectively

Qualitative customer data — interview transcripts, NPS verbatims, support ticket narratives, usability test notes, app store reviews — is some of the most valuable input you can provide to an AI for product discovery and requirements work. But it is also the input type that most product managers handle least effectively. Pasting raw transcripts or unprocessed survey exports into an AI prompt typically produces disappointing results: the AI summarizes rather than synthesizes, misses the most important insights buried in conversational filler, and cannot distinguish between off-hand comments and deeply felt pain points.

Effective use of qualitative data requires pre-processing before AI consumption. The pre-processing work separates the signal from the noise, makes the data structure visible, and prepares the AI to do analytical work rather than organizational work. The amount of pre-processing required depends on the data quality and the task: raw interview transcripts need the most work, while already-tagged support ticket exports need less.

Pre-processing interview transcripts: A raw interview transcript is typically full of conversational filler ("um," "you know," "like"), facilitator questions, social pleasantries, and off-topic tangents. For AI consumption, you need to extract the signal. The steps are: (1) Remove all conversational filler and facilitator questions. (2) Identify and keep only the "signal moments" — statements where the participant describes a problem, expresses a need, shares a workaround, or evaluates an experience. (3) Annotate each signal moment with a one-word category tag: "pain point," "workaround," "unmet need," "positive reaction," "comparison to competitor." (4) Add a "Participant profile" header at the top of each transcript: role, company size, product usage frequency, and any other segment context relevant to your research questions.

Pre-processing NPS verbatims and survey text: NPS verbatims are typically short, unstructured, and highly heterogeneous — some are actionable product feedback, some are support requests, some are general sentiment expressions. Before feeding them to AI for analysis, group them manually or semi-automatically by rough theme (you can use a first AI pass for this). "Here are 50 NPS verbatims. Group them into 5–7 themes. Return each theme as a label and a count of how many verbatims fall under it." Use the output of this first pass as the input for a deeper analytical pass: "Here are the 12 verbatims in the 'Onboarding difficulty' theme. What specific problems do they describe? Rank by frequency."

Feeding grouped verbatims for theme analysis: The most effective structure for feeding verbatim data to AI is to group them by theme or segment before sending, include the group label explicitly, and ask for analysis within each group rather than across all groups at once. "Here are 15 NPS verbatims from users who scored 3–6 (detractors and passives) and are in the construction industry segment. What product problems do these users consistently describe? Rank by frequency and severity." This targeted, segmented approach produces far more specific and actionable analysis than dumping 200 verbatims with no structure.

Hands-On Steps

Take a raw interview transcript from a recent customer interview (or a past one). Apply the pre-processing steps: remove filler, extract signal moments only, annotate each with a category tag, add a participant profile header.
Count the token reduction: how many tokens did the pre-processed version save compared to the raw transcript? For a typical 45-minute interview, you should reduce from 6,000–10,000 tokens to 800–1,500 tokens while retaining all the analytically useful content.
Export a set of NPS verbatims or support ticket texts. Run a first AI pass for rough thematic grouping: "Group these verbatims into 5–7 themes. Return each theme as a label, a count, and 2 representative verbatims."
Take the top 2 themes from the grouping pass. For each, run a second AI pass for deep analysis: "Here are all verbatims in this theme. What specific problems do they describe? What user needs or jobs-to-be-done do they suggest?"
Run a third pass to bridge to product decisions: "Based on the analysis above, generate 3 user story hypotheses that address the most frequently mentioned problems in this theme."
Evaluate the quality difference between the single-pass dump (all verbatims at once → insights) and the three-pass approach. Document the specific quality improvements to justify the pre-processing investment.

Prompt Examples

Prompt (pre-processed transcript → synthesis):

You are a senior product manager synthesizing qualitative research.

Below are extracted signal moments from 3 customer interviews. Each moment has been categorized (pain point, workaround, unmet need, positive reaction). Each set is preceded by a participant profile.

Participant 1 — Principal architect, 12-person firm, uses ProjectFlow 3x/week
[Pain point] "I have no idea what version of the drawing my site supervisor is looking at when he calls me."
[Workaround] "I email the current drawing set every Monday morning to everyone on the job — it's the only way to make sure we're all on the same page."
[Unmet need] "I just need to know that the person in the field has the latest version. That's all I care about."

Participant 2 — Project manager, 28-person AEC firm, uses ProjectFlow daily
[Pain point] "We've had two situations this year where work was done to an old specification. The rework cost us about $40K total."
[Workaround] "I print the current drawing set and physically hand it to the foreman. It's ridiculous, but it's the only thing that works."
[Unmet need] "If I could see a green checkmark next to each subcontractor's name confirming they've viewed the latest drawing set, I'd sleep better."

Participant 3 — Construction manager, 8-person specialty contractor, uses ProjectFlow 2x/week
[Pain point] "My subcontractors don't log into the platform. They work off PDFs they downloaded months ago."
[Workaround] "Every time I update a drawing, I WhatsApp the PDF to the relevant subs. It's embarrassing."
[Unmet need] "There should be a way to push the current version to everyone and require them to acknowledge it."

Synthesis task:
1. Identify the core unmet need these 3 participants share (in one sentence)
2. Describe the current workarounds and their cost (time, money, and risk)
3. Generate a "How Might We" problem statement that frames this as a product opportunity
4. Write one user story hypothesis: "We believe [feature] will [outcome] because [reasoning]"

Expected output: A tight synthesis of a shared core unmet need (current-version confirmation for distributed teams), a description of the costly workarounds, a specific HMW statement, and a testable feature hypothesis — derived directly from the pre-processed qualitative data.

Prompt (NPS verbatim → product insights):

You are a product manager analyzing NPS feedback for a B2B SaaS tool.

Below are 14 verbatim responses from NPS respondents who scored 4–6 (passive) and identified as "project manager" role. These have been pre-filtered from a larger set of 180 responses.

Verbatims:
1. "The tool is fine but I have to do too many clicks to get to my daily task list."
2. "I like the features but my team barely uses it because the learning curve is too steep."
3. "Reporting takes forever — I spend 20 minutes building the same weekly status report every week."
4. "Good for big projects but doesn't work well for smaller quick-turnaround jobs."
5. "The mobile app crashes when I try to upload photos."
6. "My client can't see project status without logging in. I just send them a PDF instead."
7. "I wish I could customize the dashboard to show only what I need."
8. "Notifications are too noisy — I turn them all off which means I miss things."
9. "The weekly status report is manual — I copy data from the platform to a Word doc every Friday."
10. "Integration with my accounting software would save me 2 hours a week."
11. "Great for office but field workers won't use it — too complex on mobile."
12. "I have to log in on two devices because there's no sync between my tablet and desktop."
13. "The reporting is basic compared to what I was using before."
14. "Would love automated reminders to subs when deadlines are approaching."

Analyze these verbatims and provide:
1. The top 3 product problems described, ranked by frequency
2. For each problem: the estimated effort impact (time cost mentioned or implied)
3. For each problem: one feature hypothesis that would address it
4. The single highest-priority problem to investigate in next discovery sprint, with rationale

Output format: Use a section for each of the 3 problems, then a "Priority recommendation" section.

Expected output: Three specific, evidence-backed problem statements (mobile complexity, manual reporting, notification noise), with effort impact estimates drawn from the verbatims, feature hypotheses for each, and a prioritized recommendation with rationale.

Learning Tip: The most common mistake with qualitative data in AI is asking for synthesis before doing the grouping step. If you send 50 or more unorganized verbatims and ask for themes, the model will identify surface-level topics (speed, usability, features) rather than specific, actionable product insights. Always do one grouping pass first, then one analytical pass per theme. This two-step approach takes no more time but produces dramatically more useful outputs.

When to Use Full Documents vs. Summarized Context

One of the most frequent judgment calls in AI-assisted product work is whether to provide a full document or a pre-processed summary as context. Getting this decision right saves time, improves output quality, and prevents the context dilution problem discussed in Topic 01. The rule is simple in principle but requires judgment in practice: use full documents for comprehension tasks; use summaries for generation tasks.

Comprehension tasks are tasks where the AI needs to read, understand, and analyze the entire document — or at least the entirety of a specific section. Examples include: "Review this PRD and flag any requirements that are ambiguous or untestable." "Read this competitive analysis and identify gaps in our positioning." "Audit these 10 user stories against the INVEST criteria." For comprehension tasks, the AI's quality is bounded by how much of the document it has access to. A summary may omit the specific detail that matters. Provide the full document (or full relevant section) for comprehension tasks — but pre-process it first to remove noise.

Generation tasks are tasks where the AI uses context as a reference point to produce new content. Examples include: "Given our product strategy, write a one-pager for the new feature launch." "Using our OKRs and competitive context, draft a roadmap narrative for the board." "Based on this user research synthesis, generate 5 user story hypotheses." For generation tasks, the AI doesn't need the full original documents — it needs the synthesized insight and key parameters. A well-written 300-word summary of a 15-page competitive analysis gives the AI everything it needs to generate positioning language or competitive messaging.

The tiered context approach for recurring task types is an extension of this rule. For task types you run frequently, establish in advance which tier of context you will provide and why. Three tiers:

Full document: Comprehension, review, and audit tasks where completeness matters
Pre-processed extract: Tasks where only specific sections of a document are relevant (e.g., feed only the "Constraints" and "Success metrics" sections of a PRD for a prioritization task)
Summary or brief: Generation tasks where the AI needs to know the facts but not the full detail

Building a decision table for your own context choices — mapping task type to context tier — takes 30 minutes and prevents you from re-making the same judgment call in every AI session. Once you have the table, context selection becomes a lookup, not a decision.

Hands-On Steps

List your 10 most frequent AI-assisted product tasks. For each, classify it as: comprehension task, generation task, or mixed.
For each comprehension task, identify the specific section(s) of the relevant document that the AI needs (you rarely need the whole document for comprehension either — just the relevant section).
For each generation task, identify the minimum context the AI needs — the synthesized facts, not the full source material. Write a 200-word context brief for the most frequent generation task on your list.
Create a two-column decision table: "Task type" | "Context approach." Fill it in for all 10 tasks.
For each task where you wrote "full document," test whether a pre-processed extract produces equivalent output quality. In most cases, it will.
Build two template context blocks for your most common recurring tasks — one for your primary comprehension task (with instructions for which sections to include) and one for your primary generation task (a structured summary template to fill in). Save them as reusable snippets.

Prompt Examples

Prompt (using summary context for a generation task):

You are a senior product manager preparing a feature announcement for internal stakeholders.

[Summary context — do not expand on this; use exactly as provided]
Feature: Automated drawing version control for construction project management SaaS
Problem solved: Project managers and field workers frequently work from outdated drawing versions, causing costly rework (avg. $40K per incident based on customer interviews)
How it works (user-facing): PMs upload a new drawing version; the system automatically notifies all tagged field workers via email with a view-confirmation link; PM sees real-time confirmation status per worker in the project dashboard
Key constraint: Subcontractors do not need a paid account to confirm receipt
Launch: Q3, targeting architecture and AEC firms

Task: Write a 200-word internal feature announcement for the sales team. Focus on: the customer pain it solves, the differentiated value (competitors do not have confirmation tracking), and the target customer profile. Include one customer quote or hypothetical testimony (clearly labeled as illustrative, not real).

Tone: Confident, customer-focused, jargon-free.

Expected output: A polished 200-word sales-team feature announcement using only the summary context — no need for the full feature specification, discovery documents, or PRD to produce usable output for this generation task.

Prompt (full document comprehension task):

You are a senior business analyst reviewing a PRD for engineering readiness.

Below is the complete PRD for the drawing version control feature. Review it in full and assess readiness for engineering hand-off.

For each of the following criteria, rate: Ready / Needs clarification / Missing
1. Clear problem statement with measurable current state
2. Defined user roles and their specific needs
3. Functional requirements — completeness and testability
4. Non-functional requirements (performance, security, accessibility)
5. Edge cases addressed (error states, empty states, boundary conditions)
6. Success metrics with measurable targets
7. Out-of-scope decisions documented
8. Dependencies identified and owned

For each "Needs clarification" or "Missing" item, write a specific question for the PM to answer.

[Paste full PRD here]

Expected output: A structured readiness checklist with specific ratings for each criterion and a list of clarifying questions for any gaps — this is a comprehension task where the full PRD is needed, not a summary.

Learning Tip: When you are unsure whether to use a full document or a summary, ask: "If a human consultant was doing this task for me, would they need to read the whole document, or would I brief them on the key points and let them work from that?" If the answer is "brief them," use a summary. If the answer is "they need to read the whole document," use the full document — but pre-process it first.

Key Takeaways

Context for AI should be purpose-built for the specific task, not repurposed from documents written for human audiences.
Use a three-tier context hierarchy: necessary (Tier 1), helpful (Tier 2), noise (Tier 3). Eliminate Tier 3 context entirely; compress Tier 2 into summaries.
A well-constructed context brief for most PM tasks should fit within 600–900 tokens — enough to ground the task without diluting AI attention.
The AI-optimized user story format adds explicit fields (Current state, Constraints, Success definition, Out of scope) that make implicit PM knowledge visible and dramatically improve acceptance criteria quality.
Qualitative data — interview transcripts, NPS verbatims — must be pre-processed before AI consumption: remove filler, extract signal moments, annotate with category tags, and add participant profiles.
For NPS and feedback analysis, use a two-step process: first grouping by theme, then deep analysis per theme. Single-pass dumps of large verbatim sets produce shallow summaries.
Use full documents for comprehension tasks (review, audit, gap analysis); use summaries for generation tasks (drafting, writing, proposing). The difference in context type produces systematically different output quality.
Build a context-tier decision table for your recurring PM tasks. Once built, context selection becomes a lookup, not a judgment call every session.