Context Building: The Art of Layered AI Problem Solving

In the rapidly evolving landscape of AI-assisted development, a powerful methodology is emerging that goes far beyond simple prompt engineering. Context Building represents a systematic approach to providing AI agents with comprehensive, layered information that enables them to solve complex problems in a single, well-informed pass.

Unlike traditional approaches that rely on iterative back-and-forth conversations with AI, Context Building invests time upfront to create a "perfect" context that equips the AI with everything it needs to generate accurate, actionable solutions immediately.

Before we dive into the details, here’s a quick walkthrough of the full AI-assisted workflow. In this video, I demonstrate how I combine screen recording, prompt generation, and codebase analysis to troubleshoot a real-world frontend issue. Check it out to see the process in action:

What is Context Building?

Context Building is the systematic process of gathering and structuring multiple layers of relevant information to provide an AI agent with complete understanding of a complex task. This approach integrates various forms of context including:

Visual Context: Video recordings, screenshots, or visual demonstrations of the problem
Codebase Context: File structures, dependencies, and architectural understanding
Content Context: Specific code within relevant files, configuration details, and implementation specifics
Behavioral Context: User interactions, expected outcomes, and failure scenarios

The Context Building Workflow: A Real-World Example

To understand Context Building in action, let's examine a sophisticated workflow for fixing a complex software bug. This multi-stage process demonstrates how different AI tools can be chained together to create comprehensive context:

Stage 1: Capture and Analyze the Problem

Start by recording a video demonstrating your frontend issue. Instead of manually writing out the problem, tools like CodeGen can analyze the video, generating detailed context and clear problem descriptions quickly. This visual evidence provides crucial context about user interactions, expected behavior, and the specific failure mode.

Stage 2: AI-Powered Video Analysis and Prompt Generation

AI tools utilize your video context to craft precise prompts. The video is processed by an AI agent with video analysis capabilities, which generates a detailed technical breakdown of the issue, identifying key components, user interactions, and potential failure points. For instance, if you're encountering synchronization issues with your search input state, the tool instantly provides a detailed, structured prompt you can use for further troubleshooting.

Stage 3: Contextual Deep-Dives with RepoPrompt and Warp

Taking it further, tools like RepoPrompt combined with Warp provide in-depth code context by scanning and indexing your entire codebase. Using the technical description as a starting point, these specialized tools search the entire codebase to identify all potentially relevant files, analyzing file structures and dependencies, identifying components related to the problem domain, gathering full content of relevant source files, and creating a comprehensive, structured prompt with all gathered information.

Stage 4: Solution Generation and Implementation

The comprehensive context is then sent to a large language model (like ChatGPT) to generate a solution. Using GPT models takes this workflow even further by providing XML diffs and accurate code snippets ready for immediate implementation. Because the AI has complete understanding of the problem, codebase structure, and relevant files, it can provide precise, actionable fixes formatted as diffs that can be automatically applied to the correct files.

The Power of Layering Context

What sets this workflow apart is its layered approach that dramatically increases accuracy and efficiency:

Initial Visual Context: Screen recordings convert visuals into AI-digestible insights
Detailed Textual Context: Automated analysis tools convert these insights into structured text prompts
Deep Codebase Integration: AI-powered code indexing tools pinpoint exactly where changes are needed

Key Benefits of Context Building

Dramatically Increased Accuracy

By providing AI with a complete picture of the problem from visual bug manifestation to underlying code structure, the likelihood of receiving correct and relevant solutions increases dramatically. The AI can understand not just what needs to be fixed, but why it's broken and how the fix should integrate with existing systems.

Enhanced Development Efficiency

Context Building streamlines the development process by reducing the need for iterative prompting and manual investigation. An end-to-end bug fix, from identification to code modification, can be completed in a single, continuous workflow rather than multiple rounds of clarification and refinement. This significantly reduces manual coding effort, making the debugging process efficient and precise.

Beyond Bug Fixes: Broader Applications

While the bug-fixing example demonstrates Context Building's power, the methodology extends far beyond software debugging:

Automated Pull Requests: Tools like CodeGen can not only suggest solutions but also automatically create pull requests, streamlining code reviews
Feature Development: When building new features, Context Building can incorporate user requirements, existing codebase patterns, design specifications, and architectural constraints
Code Refactoring: Large-scale refactoring projects benefit from Context Building by providing AI with complete understanding of current architecture and desired end state
Contextual Documentation: Integrating context-building tools into your documentation processes ensures that any team member can quickly understand the scope and state of ongoing issues
Continuous Learning and Optimization: Regularly feeding AI-driven tools with new context and feedback enhances accuracy and effectiveness over time

Technical Implementation: The Toolchain

Effective Context Building relies on a sophisticated toolchain that enables seamless integration between different AI capabilities:

Communication Platforms: Slack or similar interfaces for initial video submission and workflow orchestration
AI Agents with Multimodal Capabilities: Tools like Codegen with video analysis functionality
AI-Powered Terminals: Warp or similar tools for orchestrating codebase analysis
Context Aggregation Tools: RepoPrompt and similar specialized tools for building context-rich prompts
Large Language Models: ChatGPT, Claude, or other LLMs for final reasoning and code generation

Best Practices for Context Building

Layer Information Strategically

The power of Context Building comes from layering different tools, each specializing in a different aspect of context creation. Start with visual evidence, move to technical analysis, then aggregate comprehensive codebase information.

Invest in Context Over Conversation

Rather than engaging in iterative back-and-forth with AI, invest time upfront in building comprehensive context. This philosophy prioritizes one-shot solutions over conversational refinement.

Choose Appropriate Complexity

While Context Building is powerful, it may be overkill for simple tasks. Reserve this extensive workflow for complex problems that require deep, multifaceted understanding of systems and interactions.

The Future of AI-Assisted Development

Context Building represents a significant evolution in how we interact with AI systems. Though initially complex, this workflow quickly becomes intuitive, saving valuable time and reducing errors. By moving beyond simple prompt engineering to comprehensive context creation, we enable AI to operate more like expert consultants who have been thoroughly briefed on a problem rather than assistants who need constant guidance.

This methodology points toward a future where AI agents can handle increasingly complex, real-world problems by leveraging rich, multi-modal context. As tools become more sophisticated and integration becomes more seamless, Context Building will likely become the standard approach for serious AI-assisted development work.

The key insight is that the quality of AI output is directly proportional to the quality and comprehensiveness of the context provided. Context Building systematizes this principle, turning it from an art into a reproducible methodology that can dramatically improve the effectiveness of AI-assisted development workflows.

Give this layered context workflow a try, and you'll be amazed by how effectively AI can streamline even your most challenging coding tasks.