10 Context Documents Product Teams Should Prepare Before Building Agents (+ examples)
Without the right context, mere prompts will always under-deliver. This article outlines the various context documents product teams should prepare before delving into AI automations.
Prompt libraries are all the rage on LinkedIn these days.
I see many Product Managers and Product Marketers keep collecting them like valuable art and adding them to their trophy chest of prompts.
There’s nothing wrong with that. A well-crafted prompt can do wonders for the output you get back.
However, if you’re getting serious about leveraging AI for scale product work, especially when thinking about AI agents, prompts alone won’t take you far.
Why? Because:
If prompts are the engine, context is the fuel.
You can rev the engine as much as you want, but you’re not budging if you’re out of gas.
What do I mean by “context”?
“Context” refers to information and data that substantially guides the AI model to customize and adapt it’s response. This can be supporting materials, a body of research, a reference point, and past examples.
For example, if you need to create an AI-powered chatbot that guides a sales rep how a specific feature works (step-by-step), feeding it a few lines about your product won’t be sufficient. You would need a comprehensive product knowledge base to serve as the context.
Insufficient context is probably the primary reason some product professionals lose hope and belief in LLMs. They become sick and tired of the mediocre results that can hardly be used in deliverables.
Let me illustrate this with an example.
Here’s a sample prompt to generate an outreach email for an HRIS product called PeopleFlow:
Write an outreach email to a manufacturing company with unionized labor for PeopleFlow, an HRIS platform.
Keep the email short and easy to understand without using buzzwords.
It should have max 3-4 paragraphs with 1-2 lines each.
PeopleFlow helps with HR operations for manufacturing companies with unionized workforces.
The bolded text serves as the context here.
Here’s the response I get back from that:
Hi [Name],
I'm reaching out because PeopleFlow has helped many manufacturing companies like yours improve their HR operations.
Our platform helps with payroll processing, time tracking, and compliance management for companies with unionized workforces. Many of our customers have reduced administrative work and improved employee satisfaction.
I'd love to show you how our system works and discuss how it might fit your specific needs. Would you have 15 minutes for a quick call next week?
Best regards, [Your Name]
But when I attach a fleshed-out document with a list of prioritized ICPs, customer story, and product capability summary and attach that as context, here’s what I get back:
Hi [Name],
I noticed your manufacturing company manages a unionized workforce, which often means your plant managers manually calculate overtime using complex tiered pay structures.
PeopleFlow can encode your collective bargaining agreements into automated workflows that apply the correct union wage scales and shift differentials. Our customers have cut labor disputes by 64% and reduced compliance costs by $430,000 annually.
Our OSHA incident reporting module also generates audit-ready reports for union representatives, unlike the legacy ERP systems many manufacturers use. National Workforce uses this in their monthly reviews.
Would you be open to a 15-minute call to discuss this further?
Best regards, [Your Name]
The difference is clear. The second email speaks directly to the ICP’s pain points and references specifics from the product.
Hey, can’t I add that to the prompt?
Yes, you can. But imagine doing that 5 times every day for a year across a team of 8-10 people. It’s hard.
And this was just one example - I’ve been witnessing outsized improvements across several product and marketing assets like specs, strategy ideation, and announcement posts when a context document is available.
“AI tools aren’t good enough to create stuff I can use”
I consider myself as an AI-realist.
I maintain that there will still be things AI can’t do in the near term, e.g., stakeholder and relationship management are human-specific activities.
However, I realize many roles are going to evolve massively. Thus, for many deliverables, AI usage is quickly becoming table stakes.
I still see naysayers on Reddit:
But AI has come a long way since November 2022. The latest Open AI, Claude, and DeepSeek models are potent enough to assist with tasks like solution ideation, crafting specs, and writing product-related content. With the massive context Windows models are supporting, you don’t need to repeat much, either.
Don’t get me wrong. I’m not saying they are perfect.
But barring some limitations, they are “good enough” to accelerate production-worthy artifacts with a human-in-the-loop.
And I know. Adding context in a prompt isn’t new.
However, when I inspect the prompt history of the teams I work with, they tend to provide superficial context.
This can work for highly transactional queries like summarizing a conversation, classifying data, or transcribing a call. You certainly don’t need a context file for that.
However, suppose you’re getting into more involved processes like creating a support chatbot agent, scaling outbound motions, or building an AI-powered workflow for sales enablement. In that case, you need to a solid context-document strategy to empower a product team.
At this point, many teams have no concept of creating a centralized repository for reuse. Without a system and structure, such teams will struggle to institutionalize leveraging AI tools to accelerate their work.
The first step towards adopting AI workflows and agents isn’t signing up for the coolest agent tools. Too many folks are caught in LinkedIn FOMO.
The first step is to invest in a repository of a strong set of context documents for your product.
Alright. A quick announcement before we go on:
Product Marketing Workshop for Companies
Starting in April 2025, I’m offering an online corporate workshop (3 sessions) on Product Marketing. This is for companies looking to establish a product marketing function and train employees to apply Product Marketing in practice.
In the workshop, I’ll share how product marketing teams and leadership can create more impactful messaging, positioning, and product launches and, ultimately, rethink their go-to-market processes for the modern era.
Learn more in the workshop brief.
Interested companies can express interest using this form.
10 Context Documents worth investing in
In this article, I’ll share a few examples of context documents I’ve been working on, which have saved me a lot of time and helped me massively improve output and deliverables.
I’ve used these documents like these in use cases like:
creating a marketing website chatbot,
drafting landing pages,
building out lead magnets using tools like Lovable,
drafting presentations on Gamma, and
creating a sales enablement bot on Slack using Chatbase/Zapier.
Usage started out slow. For example, our sales enablement bot was hardly ever referenced in the initial weeks. Now, we see 10 chats a day by our sales reps and there’s an influx of requests to keep enhancing it. The CEO is invested too.
Without further ado - let’s get into these context documents.
1. Product Knowledge Base (How It Works)
Example Document: Access it here.
This document explains your product's functionality, including step-by-step tutorials, feature breakdowns, and troubleshooting guides.
Most SaaS products host their customer-facing knowledgebase on a tool like Zendesk, Zoho, Freshworks, or Hubspot. Some products allow you to export these articles into a zip file, which suffices.
Agentic tools like Zapier or Relevance AI can also absorb entire micro-sites by crawling the parent URL, so you may not need to download anything at all in such scenarios.
However, you're missing out if you still haven’t invested in creating this. Knowledgebases create a single source of truth about product capabilities, preventing misinformation and ensuring consistent understanding.
This knowledge base is typically created through collaboration between product managers and technical writers, with input from engineers and QA testers to ensure accuracy.
Usage Scenarios:
AI Customer Support: Enables the system to provide specific, accurate help with product features instead of generic responses, guiding users through actual workflows.
AI-Generated Prototypes: Creates meaningful product prototypes for incremental updates that match the existing flow of the product.
2. Company & Product Profile
Example Document: Access it here.
A company profile comes in handy, especially when creating context for external submissions like answering a RFP, setting up a product page on a directory or submitting award nominations.
This document presents a comprehensive overview of your company's mission, history, core values, and the specific problems your products solve for customers.
Building this profile matters because it helps align all AI outputs with your company's fundamental identity and ensures that generated content accurately reflects your market positioning.
The profile is typically developed by marketing teams with input from company founders, executives, and long-tenured employees who understand the company's evolution. Typically, this document doesn’t incur many changes, but the product capability footprint should be revisited every quarter.
Usage Scenarios:
Directory submissions: Helps AI generate blog posts and social media content that authentically represents your company's voice and perspective on industry trends.
Sales Proposals: Allows AI assistants to customize introductory sections that accurately represent your company's unique approach and values.
3. Strategy Doc & Priorities
Example Document: Access it here.
This document outlines your company's strategic direction, including current priorities, specific market perspectives, and the rationale behind key product decisions.
Creating this document helps ensure all AI-generated content aligns with your strategic focus and doesn't contradict official company positions.
This strategy document is typically owned by executive leadership and product management, with input from various departments, and should be updated quarterly.
Usage Scenario:
Feature Ideation: Helps AI-powered generators create feature proposals that align with strategic objectives and incorporate the company's unique problem-solving perspective.
4. Recent Vetted User Feedback
Example Document: Access it here
You might ask: why not just use G2 / Capterra links, export of a Slack channel with a feedback stream, or a Google sheet with all the user survey feedback.
In my experience, this doesn’t end well because there’s a lot of noise with feedback from poor-fit ICPs or unclear/outdated advice which throws the AI/LLM off.
For example, back in 2021, people were leveraging vFairs due to challenges faced during the pandemic. Today, we have many more users leveraging our virtual event platform because it helps their global reach and provides many engagement tools that webinar platforms don’t.
The outdated G2 reviews, while valid and positive, wouldn’t help.
This document aggregates recent customer reviews, support tickets, and user research findings to provide a current view of how customers experience your product.
Product, marketing & customer success teams typically lead the creation of this document with support specialists and product managers, updated monthly to reflect the most recent experiences.
Usage Scenarios:
Case Study Creation: Helps AI generate content that addresses real customer challenges and solutions rather than hypothetical scenarios.
Ad Creatives: If you’re building ad creatives (e.g. using a tool like AdCreative.ai), this document can provide actual customer quotes that could inform the copy on the ad assets.
5. Standard Sales Pitch
Example Document: Access it here.
This might be a subjective one, but I’ve found a sales pitch document that helps AI understand the importance you give to your product capabilities and improves the sequencing of value propositions.
A sales pitch can be derived from a call recording transcript of a model or comprehensive pitch on which you typically train your new sales reps.
It captures the proven storylines, analogies, and examples that effectively communicate your product's value to different buyer personas.
Developing this narrative helps ensure AI tools can communicate your product's value in ways that have been field-tested rather than creating theoretical explanations.
Sales enablement teams typically own this document with input from top-performing salespeople, with the narrative evolving based on win/loss analysis.
Usage Scenarios:
Email Templates: Helps AI generate personalized sales emails that follow successful narrative patterns adapted to specific prospect situations.
Presentation Building: Ensures AI creates slides that tell a coherent story about your product's value, following proven narrative structures.
6. List of Prioritized ICPs
Example Document: Access it here
The primary audience of 2 products in the same industry can be vastly different.
One extreme example is how Gusto and Workday both have HRIS capabilities, but one targets SMBs while the other is for enterprises.
Similarly, your primary buying audience might be different from the status quo. Sadly, the AI tool you’re asking for help doesn’t have that context.
That’s where an ICP document comes in. It defines your ideal customer profiles in detail.
But it shouldn’t stop at firmographics and demographics. That’s surface-level.
This document spells out the specific industry challenges, buying triggers, and pain points of your target audience.
That means instead of saying “we target Fortune 1000 companies in the US with a revenue in excess of $50 million”, it says “we focus on HR teams in organizations with more than 1000+ employees that are struggling with multi-state compliance.”
Creating this ICP document helps AI systems understand precisely who your product serves best, preventing content from targeting inappropriate audiences.
Marketing teams typically lead the development with input from sales and customer success, evolving as market focus shifts or new successful customer segments emerge.
Usage Scenarios:
Lead Scoring: Helps AI accurately identify which prospects match your ideal customer profile, focusing sales efforts on organizations most likely to succeed.
Content Creation: Enables AI writing assistants to generate targeted blog posts that address the specific pain points of priority customer segments.
7. Brand & Style Guide
Example Document: Access it here
I cannot stress enough how critical this document is for every product marketing team planning a launch.
How many times have you tried creating a launch email or social post that’s just awful and off-brand?
Yes, there is a better way. Build a style guide and democratize it across your team.
A style guide defines your brand voice, including specific language patterns, terminology preferences, and communication guidelines for consistent representation. It ensures AI-generated content feels authentically like your brand rather than generic or misaligned with established conventions.
Ex: I like to specify a list of buzzwords that no marketing collateral should be using to sound more human e.g. streamline and revolutionize.
Marketing teams typically own this document with input from communications specialists, with periodic updates as the brand evolves.
Usage Scenarios:
Social Media Generation: Ensures AI-created posts maintain your brand's distinctive voice and adhere to specific language guidelines across platforms.
Support Communications: Helps AI response generators maintain brand consistency in help desk communications, avoiding inadvertent tone shifts.
8. Competitive Research
Example Document: Access it here
Public information on competitors might be sparse in certain industries, especially when the competing solutions are walled off by a sales representative asking you for contact details.
Feeding an AI tool critical information on your competitors (that you might have extracted through dark social, niche forums, or even ghost-shopping) can help you produce better artifacts like battle cards and objection handling docs.
Typically, this document provides detailed analysis of key competitors, including their strengths, weaknesses, typical claims, and how your solution compares explicitly.
Product marketing typically owns this document with input from sales teams, updated quarterly based on competitive win/loss analysis.
Usage Scenarios:
RFP Responses: Helps AI generate answers that proactively address how your solution differs from alternatives, highlighting specific advantages.
Sales Call Preparation: Enables AI tools to generate talking points that effectively counter anticipated competitor claims for specific sales situations.
9. Market Research & Domain Guide
Example Document: Access it here
There are things specific to your domain and market that may not be easily discoverable through public archives. This is especially true when you have a vertical play or you’re operating in a market with different dynamics than the well-documented ones e.g. used car market in the Middle East is vastly different from the States.
An in-depth explanation of your market's terminology, common workflows, regulatory considerations, and industry-specific challenges is extremely useful in such scenarios.
Building this guide helps AI systems understand the specific context in which your products operate, preventing responses that lack industry awareness.
Product marketing typically leads creation with research teams and subject matter experts, updating as market shifts occur.
Usage Scenarios:
Industry Content: Ensures AI-created materials demonstrate genuine domain expertise rather than surface-level understanding of specialized terminology.
User Research Analysis: Helps AI correctly interpret customer feedback within industry norms, distinguishing between standard practices and unique pain points.
10. Roadmap / Future Initiatives
Example Document: Access it here.
This document outlines planned product developments, including specific features in progress, approximate timelines, and the strategic themes driving future priorities.
Creating this roadmap helps AI systems provide forward-looking information without making incorrect promises or discussing deprioritized features. It also allows you to cross-match this with your strategy document and see where the gaps are.
Product managers typically own this document with input from engineering leadership, updating it quarterly to reflect development progress.
Usage Scenarios:
Customer Success Updates: Helps AI generate accurate communications about coming improvements relevant to specific customer needs without overpromising.
Sales Demonstrations: Enables AI demo builders to appropriately mention upcoming capabilities with realistic timelines rather than making vague future promises.
Is this an exhaustive list of documents? Not at all.
There will be plenty of other artifacts that might make sense for your business. Sit down with your team and identify which ones need to be developed.
For example, I found a customer stories document super helpful when planning my outbound campaigns.
But how do you operationalize this?
The tricky part is weaving this into a process. Why? Some of the documents I’ve listed will quickly become stale, and you need checks and balances to ensure this doesn’t happen.
Here are a few tips:
Consolidate all the context documents into one place like a Google Drive.
Include the last updated date in the title to make it easy to assess “staleness”.
Every document must have an owner, ideally a team that would likely reference it the most. For example, the marketing/brand folks will be the best choice for managing the style guide.
Document the ownership in a place and spread it across the organization. Maintain the ownership matrics in a general channel on your Slack.
Update your SOPs to include document updation as a checklist item. For example, my product marketing team has been instructed always to update the chatbot training document whenever we launch a new feature. Similarly, the sales enablement team updates their training data whenever we serve a new use case.
Call in all teams to conduct a quarterly audit to see how fresh the documents are and the challenges people have been facing in AI outputs, ideate if new context documents are required.
Hang on. What about data privacy issues?
I hear you. This is a common problem I have to deal with.
I realize this approach may not apply to companies with policies against using LLMs and AI tools in their organization.
Due to regulatory requirements, not every team is ready or permitted to integrate these technologies. For those who do have valid concerns about privacy and data security when using AI tools, here are some considerations to keep in mind:
Create redacted versions of your context documents that remove sensitive customer information, proprietary financial data, and confidential strategy details while preserving the structural insights needed for AI tools. For example, if you can’t mention a customer’s name, describe them creatively, like “Fortune 100 company in healthcare".
Explore on-premise or private cloud AI solutions that process your data within your security perimeter rather than sending it to external providers, limiting potential exposure. I know a few folks looking into Deepseek’s open-source model for this purpose.
Implement strict governance protocols around which employees can access AI tools and what types of content can be submitted, with clear guidelines on appropriate use cases.
Hope that helps.
Till next time,
Aatir