AI assistants have become the first stop in the developer journey. A growing proportion of developers now use AI to find new tools, evaluate their options, and write implementation code. If your product isn’t showing up in these situations, you’re losing developers before they ever reach your website.

Most developer tool companies are unknowingly making it hard for AI to recommend them. There are three stages where this breaks down. We’ve found that most companies have gaps in at least one of them.

The stages:

• Discovery: when devs find products in AI search.
• Evaluation: when devs compare products with AI.
• Adoption: when devs ask AI to help with implementation.

For each stage, we’ll share examples, gaps, and actions you can take to reach more developers.

Why Your Dev Tool Isn’t Showing Up in AI Search

When developers look for new solutions, they won’t mention your product. They won’t even mention the great benefits you worked so hard to articulate.

Said no dev ever:

I need a database platform that seamlessly integrates into my workflow so I can sleep well at night.

And yet, that’s what many developer marketing landing pages look like.

AI assistants are often the first stop for developer research. But LLMs can only surface your tool if your content reflects how developers actually describe their problems.

Here’s what happens when developers search with AI:

1. The developer describes their issue to an AI assistant.
2. The AI searches its training data for concepts related to the query.
3. The AI recommends products that use the problem language.

Will AI recommend your product or your competitor’s?

Example: A developer struggles to debug when logs are scattered across multiple services. Your “centralized observability platform” solves this problem, but only if you use real developer language. If a competitor discusses distributed tracing or structured logging and you don’t, the AI is more likely to reference them in developer searches.

This isn’t just a content problem. It’s a perspective problem that affects everything from your landing pages to your docs.

Action: List the most common problems your tool solves and use those exact phrases across your landing pages, docs, and blog content.

Getting found is step one. But once a developer finds you, they’ll ask much harder questions. That’s where the next gap appears.

How You Lose Devs During AI-Assisted Evaluation

Congrats — developers have found you! Now they need to know if you or your competitors can solve their problem.

They’re not browsing features pages. They’re describing exact use cases to an LLM:

Which tools handle webhooks with retries? Do any prevent duplicate messages?

Here’s where evaluation breaks down: 

Feature lists tell the AI what your tool does, while use cases tell the AI how and when it does it — and developers are asking “how” and “when” questions. You need specifics, or developers move on.

Example: Your “reliable event processing” feature addresses duplicate webhook deliveries. Meanwhile, a competitor provides a complete example of idempotency key implementation with error handling. The AI walks the developer through the competitor’s approach in detail.

Developers aren’t evaluating marketing claims. They’re testing whether your tool fits a project they already have in mind. The more precisely your content reflects real implementation situations, the more confidently AI can recommend you.

Action: Create content around real scenarios. Authentication flows, error handling, and retry logic show not just what your tool does, but how it behaves when things get complicated.

Getting found and getting evaluated are two hurdles. But when developers are ready to build, they’ll ask AI to write the code for them. That’s where we’re headed next.

When AI Knowledge Gaps Stop Developer Adoption

Developers are convinced. Now they’re working in their editor, asking AI to help them get started with your product. 

This is the moment your documentation comes into play. If the LLM understands your product or can quickly get the context it needs, it can help developers succeed. If not, the AI assistant may block adoption.

Here’s the adoption scenario to avoid:

1. A developer asks AI to write code to adopt your product.
2. An LLM delivers an outdated version based on its training.
3. The code throws an error across the developer’s terminal.

Incomplete examples and missing context produce broken code. But that doesn’t have to be the end of the story: The developer pastes your docs into the conversation to help fix the initial problems. Will they help or will they hurt?

Example: An LLM uses outdated training data to connect an SDK, resulting in incorrect method names or deprecated parameters. Updated documentation can fill these gaps, but only if it’s easy to find and provide to the AI assistant.

Preparing your docs for AI systems is what separates the tools that get adopted from those that don’t. The bar is higher than most teams realize. Developers need docs that are findable, contextual, and ready to drop into a conversation.

Action: In addition to keeping your documentation up to date and accurate, include a “copy for LLMs” button on every page.

Developers who can quickly get accurate context for their AI assistant will succeed with your product. Those who can’t will move on to something easier.

Have you identified your blind spots yet? That’s exactly what we can help you figure out.

Where You Stand When Developers Ask AI

Discovery, evaluation, adoption: three stages where developers can slip away. Most developer tool companies have at least one of these blind spots. Many have all three. The hard part isn’t fixing them, but knowing exactly where you stand.

That’s exactly what our LLM Reach Assessment shows you. We test what AI actually says about your tool at each stage of the developer journey. See how your product compares side-by-side with your competitors, just as developers will in their AI searches.

Stop guessing at your AI visibility. Get your LLM Reach Assessment and find out exactly where you stand.