Missing Context
This is the "out-of-the-box" problem with generative AI. Models are trained on public data, so they have zero knowledge of your company's private codebase, internal APIs, or unwritten design patterns. Without workflows that "ground" the AI by feeding it this specific context, it generates generic, "one-size-fits-all" code. This code might be technically correct in a vacuum, but it's fundamentally wrong for your system, leading to immediate integration failures.
By default, an AI tool only sees the immediate file or a small surrounding window of code. It doesn't have access to your entire private repository, your Confluence documentation, your API specs, or the critical architectural decisions made in a design doc six months ago. This forces the AI to "guess" at your system's architecture, business logic, and coding standards, resulting in mismatched implementations that are syntactically right but semantically and structurally wrong.
This is a massive source of hidden rework and architectural drift. Code that looks functional fails immediately upon integration, breaking the build or causing subtle runtime errors. This wastes significant senior developer time on refactoring code that was supposed to be a time-saver. Over time, allowing this "context-free" code to be patched and merged can pollute the codebase, violate DRY principles, and create a "Frankenstein" system that is difficult to maintain.
The "Rogue" Database Call
The AI writes a new function that calls the database directly, completely bypassing the established Repository or Data Access Layer (DAL) pattern your team has standardized on.
The "v1" vs. "v3" API
The AI confidently generates code using your internal v1 user API, unaware that it was deprecated and replaced by the v3 service, causing the build to fail.
The Business Logic "Hallucination"
The AI writes code for a "new user discount" but misses the critical business rule that the discount only applies after email verification, a rule defined in a product doc the AI has never seen.
The Wrong Error Contract
The AI generates a generic 500 error response, but your internal API gateway requires a specific 422 status with a structured JSON error body, causing all upstream services to fail.
Violating Unwritten Patterns
The AI uses a standard for loop, not knowing your team's "unwritten rule" is to use functional map/filter patterns for all array manipulations to maintain readability.
The problem isn't the AI; it's the lack of a human-in-the-loop verification and governance system. These workflows are the perfect antidote.
Stop Schema Guessing
View workflow →The Pain Point It Solves
This workflow directly attacks the "guessing" problem by requiring the AI to cite file paths and schema definitions before proposing code. Instead of allowing the AI to guess at your system's architecture, this workflow forces it to reference actual source of truth documents.
Why It Works
It grounds the AI in your actual system. By requiring schema diff tools, architecture decision records (ADRs), and explicit file path citations before code generation, this workflow ensures the AI is working with your real codebase context, not its generic training data. This prevents the AI from "hallucinating" database fields, API endpoints, or architectural patterns that don't exist in your system.
Capability Grounding Manifest
View workflow →The Pain Point It Solves
This workflow addresses the "zero knowledge of private codebase" problem by creating a high-level, human-readable manifest that explicitly tells the AI what APIs, patterns, and conventions exist in your system.
Why It Works
It provides explicit context. The manifest documents your internal APIs, established patterns (like the Repository/DAL pattern), error contracts, and unwritten team rules. By feeding this manifest into the AI's context window before code generation, you're "grounding" the AI in your actual system architecture, preventing it from generating generic code that doesn't fit.
Want to prevent this pain point?
Explore our workflows and guardrails to learn how teams address this issue.
Engineering Leader & AI Guardrails Leader. Creator of Engify.ai, helping teams operationalize AI through structured workflows and guardrails based on real production incidents.