Software Estimation Accuracy: Why We're Systematically Wrong

By Priya Shankar, Head of Product at Glue

Product managers are using ChatGPT for everything from drafting PRDs to analyzing customer feedback. It has become the default AI assistant for the PM role. But if you are using ChatGPT for product managers tasks that involve your actual software system, you are hitting a wall that no amount of prompt engineering can fix: ChatGPT does not know your codebase.

This comparison explains where ChatGPT excels, where Glue fills the gap ChatGPT cannot, and whether you need one or both. For a comprehensive look at how PMs use ChatGPT today, see our ChatGPT for product managers guide.

ChatGPT (from OpenAI) became the fastest-growing consumer application in history and quickly found a home in product management workflows. PMs use it for drafting documents, synthesizing research, brainstorming, and accelerating routine tasks. Its general knowledge base covers product strategy, market analysis, and software development concepts. As a thinking partner, it is genuinely useful.

Glue takes a fundamentally different approach. Instead of general knowledge about products, Glue has specific knowledge about your product. It connects to your Git repository, parses the codebase, and answers questions grounded in your actual code. The difference is between asking "how do billing systems typically work?" (ChatGPT) and asking "how does our billing system work?" (Glue).

This is not a head-to-head competition. The two tools solve different problems using different data. Understanding which one to use for which task will make you a more effective PM.

ChatGPT is one of the most capable general-purpose AI tools available, and product managers get real value from it.

Content generation. PRDs, user stories, release notes, stakeholder communications. ChatGPT produces solid first drafts faster than any human can write from scratch. The quality is good enough that editing is faster than writing.

Knowledge synthesis. ChatGPT has been trained on an enormous corpus of product management content. It can explain RICE prioritization, summarize jobs-to-be-done theory, compare agile methodologies, and structure competitive analyses. For general knowledge, it is an excellent resource.

Brainstorming at scale. Need 20 naming options for a feature? Want to explore edge cases in a user flow? Trying to anticipate objections to a pricing change? ChatGPT generates volume and variety that accelerates creative work.

Learning acceleration. For PMs entering a new domain or learning a new framework, ChatGPT compresses the learning curve. It explains concepts clearly, provides examples, and answers follow-up questions with patience that no human colleague matches.

The fundamental difference is that Glue knows your codebase and ChatGPT does not. This distinction affects every product-specific question a PM asks.

System-specific answers. Ask ChatGPT "how does our billing system work?" and you get a generic explanation of billing systems. Ask Glue the same question and you get an answer that references your actual files, functions, and database schemas. The difference is between a textbook and a map of your building.

Feature inventory. ChatGPT cannot tell you what features exist in your software. Glue scans the entire codebase and produces a feature catalog, including features the team forgot about or never documented.

Effort context. ChatGPT can explain what a referral program typically involves. Glue can tell you which specific modules in your system would need to change, what dependencies exist, and where technical debt makes the work harder than it appears.

Dependency and risk visibility. ChatGPT has no concept of your system's architecture. Glue maps dependencies, identifies knowledge concentration risks (bus factor), and surfaces technical debt. These are the inputs that make the difference between a realistic roadmap and a fictional one.

For a broader perspective on the AI product management guide, the market includes both general-purpose AI and domain-specific tools. Each serves a different purpose.

Choose ChatGPT when your task is general-purpose and does not require knowledge of your specific system. Document drafting, competitive research, brainstorming, learning new concepts, and communication polish are all areas where ChatGPT excels. If you are a PM who needs a thinking partner for strategy and content, ChatGPT is hard to beat.

ChatGPT is also the right choice when cost is a primary concern. The free tier handles many PM tasks adequately, and the Plus subscription at $20/month is accessible for individual contributors.

Another strong use case is onboarding into a new domain. When a PM joins a fintech company and needs to understand payment processing, regulatory requirements, or banking APIs, ChatGPT can provide foundational knowledge faster than any other resource. It turns hours of research into minutes of conversation. Just remember that the answers reflect general knowledge, not your company's specific implementation.

Choose Glue when your questions are about your system. "What features do we have?" "What would this change involve?" "Where is technical debt concentrated?" "Which engineer is the only person who understands this module?" These questions require knowledge of your codebase, and no amount of ChatGPT prompting can compensate for that.

Glue is especially valuable when roadmaps slip because of hidden complexity, when estimates are consistently wrong, when PMs spend excessive time asking engineers for context, and when the organization has lost tribal knowledge through turnover.

Absolutely, and most PMs should. ChatGPT for general tasks: drafting, brainstorming, learning, and analysis that does not require system-specific knowledge. Glue for system-specific tasks: codebase understanding, effort estimation, dependency mapping, and any decision that requires knowing what the software actually contains.

The combination gives you a general AI assistant and a system-specific AI assistant. ChatGPT makes you a faster writer and thinker. Glue makes you a better-informed decision maker.

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Can ChatGPT replace a codebase intelligence tool?

No. ChatGPT has no access to your codebase and cannot answer questions about your specific system. It can explain how billing systems work in general, but it cannot tell you how your billing system works, which modules depend on it, or what changes would be required for a specific feature. Codebase intelligence tools like Glue connect to your repository and provide answers grounded in your actual code, which is a capability that general-purpose AI fundamentally lacks.

Is Glue an AI chatbot?

Glue includes a natural language interface for asking questions about your codebase, but it is not a general-purpose chatbot. It is a codebase intelligence platform that parses, indexes, and builds a semantic understanding of your entire software system. The chat interface is one way to access that intelligence, along with feature catalogs, dependency maps, technical debt dashboards, and competitive gap analysis. The AI is purpose-built for code understanding, not general conversation.

Should product managers use AI tools?

Yes. Product managers benefit from both general-purpose AI (ChatGPT, Claude) for content creation, research, and brainstorming, and domain-specific AI (Glue) for codebase visibility and system understanding. The combination addresses both the general knowledge needs and the system-specific context needs of the PM role. The PMs who are most effective in 2026 are using AI for routine tasks and investing their saved time in customer conversations and strategic thinking.