Context Creation & Maintenance Systems

2. Context Creation & Maintenance Systems

This section explores how to create and maintain context for AI assistants throughout a software project's lifecycle. We address four major challenges: (1) ensuring the AI always knows what's been done so far, (2) enabling the AI to learn from mistakes and successes, (3) helping the AI understand any given task in relation to the whole project, and (4) preventing AI-suggested changes from inadvertently breaking other parts of the system. These correspond to establishing memory, feedback loops, global awareness, and safeguards respectively.

2.1 Ensuring AI Knows Project History (Memory Systems)

The first aspect of context engineering is building persistent memory of project history that the AI can access. In practice, LLMs do not have long-term memory by default – once something scrolls out of the context window, it's forgotten. Therefore, we need systems that explicitly re-introduce relevant history into the AI's context whenever necessary.

One straightforward technique is maintaining a running log or summary of what has happened in the project and injecting that into the prompt. For example, a vibe coder might keep a Markdown file "ProjectJournal.md" where they jot down each major change, decision, or known issue after an AI interaction. Before each new query to the AI, the content of this journal (or a relevant excerpt) can be prepended to the prompt. This is a manual form of context maintenance. More automated approaches include:

• Conversation History Buffer: Many coding assistants in IDEs automatically include a window of recent conversation turns and code edits in the context. To enhance this, tools may compress older history into summaries. For instance, when using ChatGPT or Claude via an IDE plugin, the plugin could maintain a summary of earlier interactions. Summarization is a key strategy to keep memory scalable: older details get distilled, freeing space for new info while retaining crucial points . • 13 17

• Persistent Rules/Notebooks: As seen in Cursor's workflow, users leverage Cursor Rules files and Notepads to store context that persists across multiple queries or even sessions . A Cursor rules file (often .cursorrules ) can contain project-wide instructions like "We are using React and Tailwind CSS; follow their conventions" or "The database is PostgreSQL, here are the schema details…". The AI is programmed to always consider this file, effectively giving it memory of these facts. Notepads in Cursor serve a similar role for storing context on specific topics (e.g. a Notepad with all API endpoints and their usage) which the user can attach to conversations as needed . These are primitive forms of memory – essentially context caches on disk. • 18 14 14

• Model Context Protocol (MCP) and External Memory Stores: Modern implementations often use external services to provide memory. MCP is an open standard by Anthropic that allows an AI (like Claude) to connect to external tools and data sources in a structured way . For example, you could set up an MCP server for a vector database (like Pinecone or Weaviate) which stores embeddings of all project files and conversation transcripts. The AI can then query this vector DB for relevant context by semantic similarity ("fetch memory relevant to function X"). This effectively gives the AI long-term memory beyond its context window. Claude Desktop, for instance, supports one-click installation of MCP servers including a Filesystem server (to let it read/write files) and could integrate a knowledge base . By configuring Claude Desktop with an MCP filesystem, as described in the Anthropic docs, you allow it to actually recall any file from the project when needed (with your permission) . Similarly, one could integrate a project database server or documentation server that the AI can query for historical data. • 19 20 21 22 23 24

• Indexing Engines in AI IDEs: Some AI-powered IDEs include built-in indexing of the codebase. For example, Windsurf (formerly Codeium) has an Indexing Engine that retrieves context from the entire codebase, not just open files . This means if the AI needs to recall what a certain function implemented earlier does, it can search the index. Windsurf's "Memories" feature goes a step further – it persists context across conversations (even after closing the app) via both user-defined rules and auto-generated memory from past interactions . In practice, this might mean Windsurf remembers that "User is using Supabase for DB" because the user either told it once or it deduced it, and it will carry that fact into future sessions . • 25 26 27

The key is to have a single source of truth for context that the AI continuously consults or is fed from. Whether that's a living design document, a state in a database, or a file on disk, the AI should rarely have to rely on only the user's last prompt for information. By implementing these memory systems, you ensure continuity: the AI is effectively onboarded to your project like a new team member who read all the docs and commit history. This reduces instances of "Did we implement X already?" or "What was the approach we decided on yesterday?" – the AI will know if context is engineered properly.

2.2 Learning from Mistakes and Successes (Feedback Loops)

A crucial part of context maintenance is enabling the AI to apply lessons learned from previous mistakes and successes. In a coding session, the AI might attempt an implementation that fails (perhaps tests don't pass or the code doesn't compile), and the developer then finds a fix. Without a feedback loop, the AI might later repeat the initial mistake because it has no memory that it was wrong. Context engineering addresses this by capturing outcomes and feeding them back into the AI's context.

One simple practice is to explicitly tell the AI what went wrong when it makes a mistake. For example, "The last approach you gave caused a null pointer exception because we didn't check for X. Let's remember to avoid that." This instruction itself becomes part of the context going forward. However, doing this manually every time is tedious. Instead, context systems can:

• Store a Knowledge Base of Resolved Issues: Imagine maintaining a file or table of "Gotchas & Resolutions" for the project. Every time the AI hits a known pitfall and a solution is found, you add an entry (e.g., Issue: Memory leak when using library Y; Resolution: Call dispose() after use). A context engine could automatically search this knowledge base whenever the AI proposes something that resembles a known issue. In vibe coding, a developer might use an embedding-based search: the current AI suggestion can be vectorized and compared to stored embeddings of past issues to see if it's similar to something that was problematic. If a match is found, a snippet of the past experience can be provided to the AI ("Note: When we tried X before, we encountered Y, so we should do Z"). •

• Use of Test Results as Feedback: In continuous integration (CI) or even ad-hoc testing, the results can be looped back as context. If the AI writes code and tests fail, the test output (error messages, failing assertion info) is prime context to feed back. Some advanced AI coding setups run tests automatically after generation (e.g., Windsurf's Cascade can run code and then continue iterating if it fails) . That system effectively learns from the failure in real time: the failure output is in context, and Windsurf will modify the code accordingly . For those not using Windsurf, one can manually copy the error trace into the next prompt and ask the AI to fix it. But a context-aware pipeline could do this automatically: after code generation, run tests, append any failures to context, then have the AI continue. • 28 29 30

• Checkpointing and Rollback Mechanisms: Sometimes the "lesson" is that a certain path leads to worse outcomes. In the Reddit discussion about Cursor, the user mentions using Composer checkpoints to revert changes when the AI's edits start breaking things . This by itself doesn't inform the AI, but the user noted they would then start a new composer (a fresh attempt) and "ask in a different way or specify different aspects" after a revert . To close the loop, one could tell the AI in the new attempt why the previous attempt was rolled back ("We reverted because approach X started breaking Y"). Over time, even if done informally, the AI accumulates a notion of which approaches are dead-ends. • 31 32 33

Rules/Guardrails for Known Mistakes: Many AI coding tools allow setting rules. For instance, if the AI repeatedly uses an insecure function that you've banned, you can add a rule like "Do not use eval() anywhere" or "Always sanitize user input with function Z". This is a form of encoding lessons learned (maybe the AI introduced a security flaw once, and now you add a rule to prevent that class of mistake). Tools such as VS Code's Copilot Chat allow custom instructions to the model which persist (e.g., in settings one can define certain style or safety preferences). Similarly, Claude can be given a "system prompt" at start that includes these guidelines. Context engineering uses these persistent instructions to bake in lessons, so the AI doesn't need to be told twice. Windsurf's user-generated memories (rules) are exactly for this: the user can specify communication styles or APIs to use/avoid – essentially lessons for the AI to heed consistently. • 27

In essence, creating a feedback loop turns a reactive AI (one that only sees the current prompt) into a learning AI within the scope of your project. By logging outcomes and feeding them back, the AI's suggestions improve over time, resembling how a human developer gradually stops repeating mistakes after encountering them once. This dramatically boosts efficiency in vibe coding, where trial-and-error is common: the same error should ideally never be made twice by the AI.

One challenge is deciding how much detail to keep feeding back – this relates to context window management. We might not keep all past mistakes in active context forever, but we keep the important lessons. Summarizing those lessons in a concise form (like "We learned that approach A is slow due to reason B, so prefer approach C") and including that in the AI's permanent context (via a rules file or a pinned system message) is a powerful technique.

2.3 Techniques for AI to Understand Current Work in Relation to the Whole Project

Another pillar of context engineering is giving the AI a big-picture understanding of the project structure and how the current task fits into it. Humans naturally think about how a single function relates to the overall system (we know module X depends on module Y, or that this component will be used in context Z). An AI left to its own devices might treat each task in isolation, which can lead to designs that don't integrate well or duplication of functionality.

To address this, we need to provide context that spans the entire project:

• Project Maps and Architecture Diagrams: Providing the AI with an overview document of the project's architecture can be extremely helpful. This could be a simple text outline of the system components and their relationships, or even embedding an actual diagram (perhaps described in text form). For instance, a "SystemOverview.md" might list: "Frontend (React) -> calls Backend API -> interacts with Database (tables listed) -> uses external Auth service," etc. By including this in the AI's context (especially at the start of a session or whenever major context loss might occur), the AI can reason about how new code will interact with existing pieces. In vibe coding practice, a developer might manually remind the AI: "Recall our architecture: the client sends JSON to the Flask API which writes to Postgres. The code you write should fit into that pattern." A context engine could automate this by always prepending a saved architecture summary to relevant prompts. •

• Associated File Awareness: Advanced AI coding tools employ strategies to let the AI see related files. For example, when editing a function in one file, workspace search context can be provided – such as "here are all the references to this function across the codebase." VS Code's Copilot Chat has a #usages tag that can pull symbol references across the workspace . If an AI is about to refactor a function, including its usages in context helps it understand impact. Similarly, an • 34

#include of an entire file or #codebase search for a keyword can retrieve matches . Context engineering orchestrates these retrievals automatically. For instance, an extension could detect that the current user query is about "improve function Foo" and behind the scenes attach the content of Foo and maybe a couple calls to Foo from elsewhere to the prompt. Cursor and Windsurf do this kind of thing by default: Cursor's retrieval models "find context" by pulling in relevant files so the user doesn't have to manually paste them . Windsurf's local indexing similarly ensures suggestions are based on the whole codebase context, not just open files . 35 36 25

• Dependency Graph & Impact Analysis: To truly grasp the whole project, an AI benefits from knowing the dependency graph – which modules depend on which, and in what way. A context maintenance system might integrate a tool like a graph database (Neo4j) to store relationships: e.g., Function A calls B, Module X imports Module Y, Service M reads from Database table N. If such a graph is accessible (via an MCP server or by exporting it as a text adjacency list), the AI can query it. In practical terms, you might implement a command like "#graph find impacts of changing function Foo" which returns a list of all modules and functions affected. The AI, seeing that in context, will avoid breaking those or at least highlight them. Neo4j was mentioned as a possible context store for relationship mapping, reflecting this idea of using graph queries as part of context . • 19 20

• Current Task Contextualization: The developer or a context engine should always clarify how the current task relates to user requirements or higher-level goals. For example, if the current coding task is "Add a login feature," the AI should be given context from the product requirements (like "login requires username+password, 2FA, and should integrate with existing user profile system"). If that requirement was documented in a PRD (Product Requirements Doc), pulling a summary of it into the prompt grounds the AI's solution in the actual need, not just generic implementation. Context engineering might use something like: having all relevant requirement/ user story text in a knowledge base and doing a keyword match or semantic search when a related coding task comes up. E.g., if the branch or commit message contains "login feature", fetch the requirements paragraphs about login from Notion or Google Docs. This ensures the AI doesn't implement something that doesn't meet the real intent (a common risk if it works in a vacuum). •

In vibe coding, often the "bigger picture" can be easily lost since the focus is on one small problem at a time. By systematically providing project-wide context and clarifying relationships, we help the AI produce code that is cohesive and integrated. The outcome is less "franken-code" and more harmonious with the rest of the system. For example, if adding a new API endpoint, the AI that knows the conventions of all existing endpoints (naming, error handling, etc., because they were provided as context or accessible via search) will create a new endpoint in a consistent manner.

2.4 Strategies to Prevent Breaking Changes Across Interconnected Modules

Preventing breaking changes is a specific but vital outcome of context engineering. A breaking change occurs when an AI's modification in one part of the codebase causes failures in another part (e.g., changing a function signature without updating all calls, or altering a data structure without migrating usage). Humans mitigate this by remembering to update references or by running tests – we need to impart similar caution to an AI.

Strategies include:

• Automated Reference Updates: If the AI is asked to change something that has ripple effects, an intelligent context system will also surface all the places that need changing. For instance, suppose we ask the AI to rename a database column or refactor a function's parameters. The context engine should pre-emptively fetch all occurrences of that column or function across the project (via a search index or AST analysis) and feed them in, so the AI is aware of everything that needs adjustment. This could prevent the scenario where the AI renames it in one model file but not in others. In practice, this means doing a multi-file edit, which some tools support. Cursor's "Composer" mode actually is capable of making changes in multiple files automatically when given a high-level instruction . Users have noted that using Composer, Cursor can update all necessary files in one go (essentially it maintains its own plan/checkpoints of what to edit) . Ensuring the AI has global knowledge of usage is key – as mentioned in 2.3, including #usages or cross-file references in context is part of this. • 37 38

• Test-Driven Context: Another powerful mechanism is to leverage tests as context. If your project has a test suite, including relevant test files in the prompt (when the AI is editing a feature) can warn the AI about the intended behavior and invariants. For example, if there's a test that expects function computeTax to throw an exception on negative input, and the AI is refactoring computeTax , having that test code in context will remind the AI to preserve that behavior. Also, running tests after changes (as mentioned earlier) and feeding failures back will catch breaks. It's essentially a quick feedback loop: break -> context -> fix. Some advanced flows might even have the AI read test results and propose fixes autonomously (a bit like an AI-driven continuous integration).

• Schema and Contract Awareness: Many breaking changes come from not understanding implicit contracts – e.g., the shape of a JSON payload expected by frontend from backend. If the AI decides to change a response format, it might break the frontend. To avoid this, treat schemas and interface definitions as sacred context. Always provide them when relevant. Tools like OpenAPI/Swagger specs for APIs, database schema definitions, or interface definitions in strongly typed languages should be part of the context. This way, the AI knows "if I change this, everything that depends on it must change." In fact, we can instruct the AI explicitly in context: "Do not change public interface definitions unless instructed" as a guardrail. Or, "If you change this schema, list all dependent components." In an enterprise context, there are often clear BRDs (Business Requirement Docs) specifying what should not change; feeding those to the AI acts as a break-check.

• Version Control & Diff Review: Another context strategy is using git diffs. If an AI is about to commit a change, having it review a git diff (which could be shown to it as context) against the previous version might allow it to catch obvious breaking changes. For example, if it removed a function that was being used, the diff will show deletion of that function and perhaps references. While AI doesn't automatically know those references are unresolved, a smart prompting might say: "Here's the diff, ensure that all changes are consistent and nothing is left calling removed code." This mimics a code review process and helps the AI self-audit its changes. Some tools attempt this with "checkpoints" (like the revert approach in Cursor). Alternatively, a continuous integration agent could analyze the diff and comment, which then gets fed to the AI.

• Graph or Dependency queries: Building on the earlier suggestion, if we maintain a graph of dependencies, we can query it before making a change. For example: before the AI executes "remove method X", the context system can do:

Option 3: Use a code block with explicit language

match (m:Method {name:"X"})-[:CALLED_BY]->(n) return n in a graph DB, yielding all methods that call X.

Present that list to the AI: "Methods A, B, C call X – ensure they are handled." This alerts the AI to either update those or at least notify the user.

Many vibe coders already intuitively do scaled-down versions of this by asking the AI follow-up questions like "Does anything else need to be changed to support this update?" But by engineering context to automatically cover these bases, we reduce oversight. The result is fewer broken builds and a smoother flow.

One real-world anecdote: a user on Reddit described that their Cursor project would become unmanageable ~once it hit ~20 files and multiple languages, leading to loops of errors . Part of the reason was likely the AI not keeping track of all the interactions between those files. The advice given in the thread was to break up the work logically and to use the rules file as "glue" to record decisions, and also to commit often and roll back when needed . This highlights that without holistic context, complexity overwhelms the AI. The techniques here – giving the AI a holistic view – directly tackle that complexity. 39 40

In conclusion, preventing breaking changes is about making the AI context-aware of the ripple effects of any change. By always providing context on dependencies, tests, and interfaces, and by employing feedback loops like diff checks, we empower the AI to act responsibly in a large codebase. This turns the AI into a more reliable collaborator rather than a junior that constantly needs human supervision to catch breakages.