A student showed me their first AI tool recently.

After a fair amount of prodding and haranguing. They were hiding something!

We hopped on a call and they were gutted - "the outputs are rubbish," they said. They wanted to trash it all and start again.

First we had a look into the tool. The prompt was solid, the logic was sound, but something was missing.

Turned out they'd just dumped their raw prompt into the platform.

No context. No instructions. No background knowledge.

It was like hiring an expert consultant but not telling them anything about your business or what you actually needed.

We added some basic instructions and background info - suddenly the same prompt was producing brilliant results. The difference wasn't the prompt - it was giving the AI the additional context it needed to actually help.

Let’s get started:

Adding context

• Why raw prompts often disappoint

• The Goldilocks principle of context

• Finding the right level of specificity

• What information to provide

• Testing and refining instructions

The Goldilocks Principle of Context

Think about hiring someone on Upwork to write proposals for your business. What would they need to know?

Basically you are answering the question: "If a human was doing this task for the first time, what would they need to know?"

Then:

1. Write down everything you think they'd need

1. Cross out anything not directly relevant to the task

1. Add any task-specific requirements or constraints

1. Include style/tone guidance if output format matters

Too little context ("Write me a proposal") and you'll get generic waffle. Thanks ChatGPT!

Too much ("Here's every proposal we've written since 2015, our entire company history, and a breakdown of our competitor landscape") and they (the human or the AI!) will get lost in the details.

The sweet spot? Exactly what they need to know for THIS task:

• Your company's relevant experience for THIS project

• Previous successful proposals for SIMILAR work

• The specific tone and style you want

• Any particular requirements or constraints

Adding Context Through Knowledge Bases

Modern AI platforms make it easy to give your tools additional context. Whether you're using Claude Projects, LaunchLemonade, or any other tool, you can typically add context in two ways.

Project-Level Knowledge:

• Company information

• Brand guidelines

• General policies

• Standard processes

• Reusable reference materials

Tool-Specific Knowledge:

• Task-specific data

• Relevant examples

• Specific constraints

• Output format requirements

Think of project-level knowledge as what ALL employees at your company should know, and tool-specific knowledge as what someone needs for this particular task. We need to provide both.

Let's use this prompt to figure out exactly what context your tool needs:

Help me identify what context an AI tool needs to perform its task effectively. Analyse the following tool purpose and provide three levels of context: Tool Purpose: [Describe what your AI tool will do, including inputs, outputs and processes] For this type of tool, provide:
1. Minimal Context (bare bones, likely to produce poor results)
2. Context Overload (too much irrelevant information)
3. Perfect Context (exactly what's needed) For the "Perfect Context" level, break down exactly what information should be provided and why it's necessary for the task. Sort by:
- Essential company/product information
- Relevant background knowledge
- Style/tone requirements
- Task-specific parameters
- Any relevant constraints or limitations Explain why each piece of information is necessary for the task.

Let's look at some examples to make this more concrete.

Marketing Copy Tool

❌ Too little: "Write social media posts for our product"

❌ Too much: [Entire company history, every past post, all competitor data]

✅ Just right:

• Target audience demographics and preferences

• Key product benefits and features relevant to THIS campaign

• Brand voice guidelines (casual, professional, witty?)

• Campaign goals and CTAs

• Platform-specific requirements

Product Recommendation Engine

❌ Too little: "Recommend products based on user input"

❌ Too much: [Every product spec, all customer reviews, full pricing history, all past transcations]

✅ Just right:

• Current product catalogue with key features

• Basic price points and availability

• Common use cases and customer needs

• Any current promotions or focus products

• Recommendation criteria (price, features, popularity)

Get the idea? Here's are some quick rule of thumbs to help you know if you are on the right track.

Too little context if:

• Outputs are generic

• AI asks lots of clarifying questions

• Results miss key requirements

Too much context if:

• AI gets distracted by irrelevant details

• Outputs are inconsistent

• Responses meander off topic

Just right when:

• Outputs are specific and relevant

• AI stays focused on the task

• Results match your requirements

How will you know?

Because you are a subject matter expert remember? You are building for an industry that you know.

This is crucially what differentiates your tool from a generic one spun up by AI. You have the discernment and taste to know if the outputs are good or not!

We’ll build on this tomorrow when we get into testing properly.

PS. If you’ve got this far we’re exploring launching a 30 Day AI Entrepreneurship Accelerator where we:

1. Hone a business idea

2. Build a focused AI tool

3. Test and refine the tool

4. Market and launch

Course, community and live sessions.

Waitlist here: https://promptentrepreneur.beehiiv.com/c/waitlist