What You Will Learn

• Set up the three core directories: AI docs/, specs/, and .claude/ (or equivalent)
• Write specs as executable plans so "the plan is the prompt"
• Create reusable context-priming prompts to boot AI sessions reliably
• Enable self-validation loops so agents test and verify their work
• Scale engineering output by handing off full features to AI agents

Study Guide

Introduction: Why Context-Driven Agentic Coding Matters

Imagine an AI coding assistant that doesn’t just spit out snippets, but understands your entire project, plans complex features, tests its own work, and hands you back results with confidence. This isn’t science fiction,it’s the reality of agentic coding. The secret? Systematic context management.

This course is your deep dive into the practice of structuring your codebase,using three essential directories,to unlock the full power of agentic AI coding tools. Whether you’re working with Claude, Cursor, Fline, Codeex, or another tool, the principles you’ll learn are tool-agnostic and proven to scale your engineering output. You’ll learn why “Context is everything. Context is king.” and how to deliver context so your AI tools can plan, build, and validate work at a level that puts you in the top 1% of AI-powered developers.

By the end, you’ll walk away with a concrete, actionable workflow to transform your role from code generator to curator of engineering excellence, leveraging AI as your capable partner rather than a mere assistant.

The Foundation: Understanding Context in Agentic AI Coding

Context isn’t just helpful for AI,it’s essential. It’s the difference between an agent that flounders and one that delivers results indistinguishable from a senior engineer’s work.

In AI coding, “context” means the information, documentation, plans, and patterns the AI references to understand what to do. The richer and more relevant the context, the more effective your AI agent becomes.

But there’s a catch: Too little context, and your AI will guess, hallucinate, or miss critical details. Too much, and it gets lost, overwhelmed, or confused. The art lies in delivering the right context, at the right time, in the right format.

Example 1:
If you ask your AI assistant to “add Stripe payments,” but the agent can’t see your current payment flow, API keys, or project structure, it can’t deliver a reliable solution.
Example 2:
If you dump your entire project,thousands of files,into the AI’s context window, it may miss the key files or lose track of the main objective.

The solution: Structure your codebase with directories that systematically deliver essential information, plans, and reusable prompts. This is what the top 1% of AI engineers do.

The Three Essential Directories: The Blueprint for Top 1% Agentic Coding

To consistently deliver the right context, you’ll set up three key directories in your codebase:

1. AI docs/: Persistent knowledge and memory for your AI agents.
2. specs/: Where you define detailed plans and specifications,the single most important folder.
3. .claude/ (or equivalent): Home for reusable, runnable prompts that supercharge your workflow.

Let’s break down each one in depth, with practical examples, applications, and best practices.

1. The AI Docs Directory: Your AI’s Persistent Memory

“What goes inside of AI docs? Here you have third-party API documentation, integration details, custom patterns and conventions, any implementation notes, anything specific to your codebase, it all goes in AI docs.”

The AI docs/ directory acts as a living, breathing knowledge base for your project. It’s the first place your AI agent looks when it needs to understand how your codebase works or how to integrate new features.

Purpose:
To store documentation, integration details, API docs, internal conventions, and codebase-specific notes that your AI tool can instantly access,without you having to feed it manually each time.

What to Store in AI docs/:

• Third-party API documentation (e.g., Stripe, Twilio, AWS, custom services)
• Integration details (e.g., OAuth flows, webhook endpoints, authentication requirements)
• Custom code patterns and architectural conventions (e.g., “We use Repository pattern for data access”)
• Implementation notes and gotchas (e.g., “Avoid using X library due to legacy conflicts”)
• Project-specific terminology or business logic explanations

Example 1:
A file AI docs/stripe.md containing your Stripe integration keys setup, webhook handling logic, and links to the Stripe API reference. When you ask your AI to “add a new payment event,” it instantly references this file.

Example 2:
AI docs/patterns.md documents your team’s convention for error handling, so the AI can follow your standard when generating new functions.

Practical Applications:

• When onboarding a new AI agent, it can ramp up on your project without manual hand-holding.
• When you switch contexts (e.g., frontend to backend), the AI can quickly access relevant design decisions or integration notes.
• For complex, multi-service projects, the AI can see how different pieces fit together.

Best Practices:

• Organize files by topic or service for quick lookup (e.g., AI docs/payments.md, AI docs/auth.md).
• Keep docs concise but comprehensive,summarize where possible, but don’t omit crucial details.
• Update AI docs/ whenever you make significant architectural or integration changes.
• Don’t overload with obsolete information; prune regularly.

Tip:
AI docs/ is most valuable during initial setup, onboarding new agents, or when integrating new third-party tools. Once the information is deeply embedded in your code, its role shifts to “quick reference” rather than daily driver.

2. The Specs Directory: The Engine of Your Agentic Workflow

“The specs directory is the most important folder in your entire codebase. This is where we write great plans. This is where we scale up our compute and do more work than ever in single massive swings.”

The specs/ directory is where the magic happens. Here, you define the “plan as the prompt.” Instead of asking the AI for small, iterative changes, you give it the whole plan up front,enabling it to plan, build, and test entire features in one go.

Purpose:
To store detailed specifications, PRDs (Product Requirements Documents), and step-by-step plans for features, bug fixes, or system changes.

What to Store in specs/:

• High-level feature specs (“Add multi-user chat with notifications”)
• Step-by-step implementation plans (“1. Set up WebSocket server, 2. Implement client listeners, 3. Add database schema for chat logs…”)
• Bug fix outlines (“Steps to reproduce, expected behavior, plan for patching, regression tests required”)
• Product requirement documents with user stories, acceptance criteria, and test cases
• Technical design decisions for complex changes

Example 1:
specs/add-stripe-subscriptions.md describes:

• Goal: Add support for recurring subscriptions using Stripe
• Requirements: List user flows, edge cases, error handling
• Plan: API changes, UI changes, database migrations, test plan
• Validation: “Agent must run all subscription scenarios in test mode and verify with Stripe’s test webhook events.”

Example 2:
specs/fix-session-timeout-bug.md details:

• Steps to reproduce the session timeout bug
• Expected vs. actual behavior
• Plan for patching: Update expiration logic, add tests, verify logging
• Self-validation: “Ensure automated tests pass and manual session expiry scenarios behave as expected.”

Practical Applications:

• Hand off a complete feature build to your AI agent,no endless back-and-forth prompts.
• Enable your agent to plan, build, and test an entire workflow, freeing you to review and refine the result.
• Collaborate with the AI during plan drafting: you provide high-level requirements, the agent drafts the plan, and you iterate together before execution.

Best Practices:

• Write plans with enough detail to eliminate ambiguity, but concise enough to fit the AI’s context window.
• Use checklists, bullet points, and stepwise instructions.
• Include validation or “done” criteria so your agent knows when the work is finished.
• Iterate on the plan: start with a draft (AI or human generated), refine, then execute.

Quote:
“The plan is the prompt and great planning is great prompting.”

Tip:
You can scale your engineering work by an order of magnitude by switching from task-by-task prompting to handing off entire, well-defined plans. This is where the “compute advantage” of agentic coding becomes real.

3. The .claude Directory (or Equivalent): Reusable, Runnable Prompts

“The most important reusable prompt that I recommend you set up inside of all your codebases is the context priming prompt.”

The .claude/ directory (or whatever you want to call it for your tool) is your repository of reusable, command-like prompts. These are not just “instructions”,they’re scripts the AI runs, often at the start of a session or for recurring tasks.

Purpose:
To store prompts that automate common setup, context priming, or workflow steps, ensuring consistency and saving you from repeating yourself.

What to Store in .claude/:

• Context priming prompts (“Read the README, run git ls-files, summarize key directories.”)
• Setup commands (“Install dependencies, check for environment variables, verify code style.”)
• Reusable workflow steps (“Run all tests and report failures,” “Generate changelog from latest commits.”)
• Custom linting or formatting commands
• Any prompt you find yourself retyping regularly

Example 1:
.claude/context-primer.txt contains:

• “At the start of every session, do the following: Read the README, list all files in the project, highlight the main directories and their purpose, summarize any existing specs in specs/ and any docs in AI docs/.”

Example 2:
.claude/run-tests.txt:

• “Run all automated tests. Report any failures and suggest fixes. If all pass, output ‘All tests passed,ready for review.’”

Practical Applications:

• Instantly set up the AI’s session context, even after switching branches or projects.
• Onboard new engineers or agents by giving them a single command to see the whole project.
• Ensure every session starts with the AI “knowing” where everything is,reducing errors and missed context.
• Automate repetitive tasks, freeing you to focus on review and higher-level direction.

Best Practices:

• Keep each prompt focused,avoid combining unrelated tasks in a single command.
• Regularly review and update prompts as your workflow or codebase evolves.
• Share effective prompts across projects to standardize your engineering process.
• Remember: The .claude/ concept is tool-agnostic. Use whatever folder name fits your tool (e.g., .cursor/, .agent/, .prompts/).

Tip:
The single most valuable prompt: the context priming prompt. This one command sets up your entire session, so your agent knows the project’s structure and current state. Make this the default in every codebase.

The Agentic Coding Paradigm: Beyond Simple Code Generation

“I’m not just writing prompts that generate code. I’m writing prompts that do engineering work. That’s building. That’s planning. That’s testing. It’s the whole development life cycle.”

Agentic coding is a leap beyond traditional “AI coding.” Instead of writing prompts that spit out code snippets, you’re giving your AI agent tasks that span planning, building, and even validating its own work.

AI Coding:
You: “Write a function to validate emails.”
AI: Writes the function.

Agentic Coding:
You: “Here’s a spec for user registration, including email validation, confirmation emails, and error handling. Plan, build, and test the entire workflow.”
AI: Reads the plan, references AI docs/, runs context priming prompt, builds the feature, writes tests, runs validation, and reports results.

Key Agentic Capabilities:

• Self-validating loops: The agent tests its own work against the plan’s criteria.
• Multi-tool orchestration: It runs commands, scripts, or external tools as needed.
• Handles the full lifecycle: Planning, building, testing, and reporting,all in one flow.

Example 1:
Your specs/add-multi-language-support.md includes steps for updating translations, modifying UI layouts, and testing locale fallbacks. The agent executes the plan, runs tests, and verifies all languages display correctly,without you intervening at each step.

Example 2:
You draft specs/upgrade-database-schema.md with migration steps and rollback strategy. The agent validates the migration, checks data integrity, and runs post-migration tests, reporting any failures.

Best Practices:

• Always define “done” criteria or validation steps in your specs so the agent can self-check its work.
• Review the agent’s output not just for code quality, but for adherence to the plan and successful validation.
• Use the agent to draft plans, but iterate and refine before execution.

Plan Drafting: Collaborating with AI to Create Effective Specs

“The big difference here is that both myself and my AI coding tool are going to be part of drafting this plan.”

Plan drafting is a game-changing workflow. Instead of writing complex specs from scratch, you collaborate with your AI:

1. You provide the high-level goal or requirement.
2. The AI drafts the initial plan, referencing the codebase, AI docs/, and reusable prompts.
3. You review, clarify, and refine the plan, ensuring nothing critical is missed and all details are accurate.

Example 1:
You: “We need to add two-factor authentication using Twilio SMS.”
AI: Reads AI docs/twilio.md, inspects the current auth flow, and drafts specs/add-2fa.md with implementation steps, error handling, and test cases.
You: Add notes about edge cases, clarify user experience details, and adjust the test plan.

Example 2:
You: “Fix the intermittent timeout when uploading images.”
AI: Reads codebase, drafts specs/fix-image-upload-timeout.md with repro steps, proposed fixes, and validation plan.
You: Add extra logging requirements and clarify the deployment strategy.

Benefits of Plan Drafting:

• Leverages the AI’s ability to quickly understand the codebase and propose steps you might overlook.
• Reduces human error and ensures alignment between intention and execution.
• Accelerates the specification process, letting you focus on review and refinement.

Best Practices:

• Always review and iterate on AI-drafted plans,never accept the first draft blindly.
• Encourage the AI to reference AI docs/ and relevant specs in its draft.
• Use the iterative process: Draft, review, refine, then execute.

Context Priming: Setting Up Your AI for Success

“Having too much context is just as bad as not having enough. Having too much context can confuse your agent. Having too little won't let them get the job done.”

Context priming is about giving your AI agent the essential information it needs at the start of every session or before executing a plan.

What does a context priming prompt do?

• Reads the README or other core documentation
• Lists all files and directories (e.g., via git ls-files command)
• Summarizes key project areas
• Highlights any active specs or docs relevant to the current task

Example 1:
.claude/context-primer.txt: “At the start of every session, read README.md, run git ls-files, summarize the main directories, and list any active plans in specs/.”

Example 2:
.claude/onboarding.txt: “For a new engineer, output a summary of the codebase’s structure, active specs, and links to integration docs in AI docs/.”

Best Practices:

• Keep your context priming prompt up-to-date as your project evolves.
• Trigger the priming prompt at the start of every major session, branch switch, or before running a new plan.
• Don’t overload the AI with too many files,focus on the essentials.

Self-Validation: The Key to Reliable Agentic Workflows

“We have self validation on. So I can pretty much be guaranteed I can be assured that everything here works, right? All the tests passed. Our agentic coding tool is testing itself.”

Self-validation is a hallmark of agentic coding. It’s not enough for the AI to build features,it must test and verify its own work, so you can trust the output before it hits production.

How to Enable Self-Validation:

• Include “done” criteria and test steps in your specs/ plans.
• Use reusable prompts in .claude/ to run all tests and report failures.
• Have your AI agent output a validation report at the end of each task.

Example 1:
Your specs/add-user-roles.md includes: “When finished, run all user-related tests and verify role permissions by simulating user actions. Report pass/fail results.”

Example 2:
Your .claude/validate-deployment.txt prompt runs post-deployment checks and notifies you of any regressions.

Benefits:

• Reduces manual QA cycles
• Catches errors early,before code review or deployment
• Increases trust in agentic tools and enables larger handoffs

Best Practices:

• Always specify validation steps in specs/.
• Automate test running and reporting via .claude/ prompts.
• Review validation results as part of your code review process.

Scaling Compute: Doing More by Working Smarter

“By implementing these structures and adopting the recommended workflow, you can scale up our compute and do more work than ever in single massive swings, passing off significant engineering tasks to your AI agents.”

The ultimate benefit of this approach is multiplying your output. By codifying context, plans, and reusable prompts, you enable your AI to handle large, complex tasks,freeing you to act as a curator and reviewer, not just a coder.

Example 1:
Instead of spending a week on a new feature through dozens of iterative prompts, you hand off a complete plan in specs/ and get a fully built, tested feature in a single pass.

Example 2:
You maintain several projects. By standardizing AI docs/, specs/, and .claude/ across them, you can “spin up” new agentic sessions in minutes, not hours.

Best Practices:

• Refine your directories as you learn what works best for your projects.
• Periodically review your specs/ and AI docs/ for outdated or redundant content.
• Share effective prompts and plans with your team to scale best practices.

Managing Context: Precision Over Volume

“Having too much context is just as bad as not having enough.”

Context window limitations are real. Give your AI agent too much information, and it can get confused or ignore what matters. The solution: Be deliberate and precise about what you store in AI docs/, what goes into each plan, and what you include in context priming prompts.

Tips for Managing Context:

• Summarize, don’t dump. Point the AI to relevant files or sections, not everything at once.
• Use “reference links” in specs/ (e.g., “See AI docs/auth.md for OAuth details.”)
• Prune irrelevant or outdated docs regularly.
• When in doubt, ask the AI to summarize key points from long docs before proceeding.

Example 1:
Instead of feeding the AI the entire node_modules/ directory, reference AI docs/dependencies.md with only the critical dependencies.

Example 2:
When adding a new feature, only include the relevant sections of your codebase or specs in the context priming prompt.

Tool-Agnostic Approach: These Patterns Work Everywhere

“Although the video uses Claude Code as an example and names one directory .claude, the underlying principles of using dedicated directories for documentation, plans, and reusable prompts apply to any AI coding tool.”

The directory structure outlined here isn’t tied to any one tool. Whether you’re using Claude, Cursor, Fline, Codeex, or a custom solution, the logic is the same:

• AI docs/ – Persistent project memory
• specs/ – Plans and specs as executable prompts
• .claude/ (or your tool’s name) – Reusable, runnable prompts

Example 1:
On a Python project using Cursor, you might use .cursor/ for prompts, but the workflow is identical.

Example 2:
On a JavaScript project with Codeex, you name the directory .codeex/, but store the same types of reusable prompts.

Best Practices:

• Name directories according to your tool, but keep the structure and purpose consistent.
• Standardize directory usage across projects for faster onboarding and scaling.

Putting It All Together: The New Agentic Workflow

Here’s how your workflow changes:

1. Prime your AI tool with essential context using your reusable context priming prompt from .claude/.
2. Draft a detailed plan for your task or feature in specs/,start with an AI-generated draft, then iterate.
3. Refine the plan until it’s clear, actionable, and includes validation steps.
4. Hand off the plan to your agentic coding tool for implementation, testing, and validation.
5. (When needed) Reference AI docs/ for third-party or project-specific knowledge.
6. Review the output, focusing on adherence to plan and test results, not just code quality.

Example:
You’re building a new onboarding flow. You:

• Run the context priming prompt to set up the session.
• Draft specs/add-onboarding-flow.md with your requirements.
• Have the AI refine and expand the plan, referencing relevant docs.
• Hand off the plan for implementation and automated validation.
• Review the finished work and deploy with confidence.

Result:
No more endless micro-prompts. You’re leveraging your AI’s full capabilities, scaling your output, and shifting your role to high-leverage review and curation.

Advanced Applications and Extending the Pattern

These directory patterns aren’t just for code,they can be adapted for non-coding AI tasks as well.

• AI docs/ can store business process docs, SOPs, or policy references for an AI writing assistant.
• specs/ can hold editorial calendars, campaign plans, or analysis blueprints for AI-driven marketing or analytics projects.
• .claude/ can store reusable prompts for summarization, report generation, or workflow automation.

Example 1:
In a customer support AI project, AI docs/ contains escalation policies, specs/ has incident response playbooks, and .claude/ includes prompts to auto-generate daily summary reports.

Example 2:
For an AI-powered market research assistant, AI docs/ stores reference materials, specs/ holds research plans, and .claude/ automates recurring analysis tasks.

Mindset Shift: From Coder to Curator

With this structured approach, your role evolves:

• From coder to curator of information and plans
• From writing endless prompts to reviewing and refining plans and outputs
• From micro-managing the AI to empowering it with context and trust

Quote:
“It’s really about the patterns. It’s about the principles. It’s about how you approach this new age of engineering.”

Conclusion: Unlocking the Full Potential of Agentic AI Coding

You’ve now seen, in detail, how three directories,AI docs/, specs/, and .claude/,can elevate your AI coding workflow from reactive to agentic, from piecemeal to systematic. By managing context with precision, drafting and refining great plans, and making your prompts reusable, you unlock a new level of scale and reliability in your engineering work.

These patterns are not just about organization,they are about leverage. They let you do more with less manual intervention, reduce errors, and free your time for high-value decision-making and review. You become the curator, not the bottleneck, and your AI tools become reliable partners in building, testing, and delivering complex solutions.

Key takeaways:

• Context is king,structure it with AI docs/ and targeted prompts.
• The plan is the prompt,specs/ is where you scale your output.
• Reusable prompts in .claude/ (or equivalent) make your workflow consistent and efficient.
• Agentic coding is about handing off entire engineering cycles, not just code snippets.
• Self-validation closes the loop, ensuring you can trust your agent’s work.
• Precision matters,more context isn’t always better. Curate, focus, and update regularly.

Apply these principles, and you’ll experience a fundamental shift in how you work with AI,turning your codebase into a living, evolving system that any capable agent (or engineer) can pick up and run with. This is how you reach the top 1% of AI-powered development. Now, put these patterns to work,and watch your output, confidence, and impact grow.

Frequently Asked Questions

This FAQ section covers the essential principles, tactical strategies, and practical applications of “Agentic Claude Code: 3 Codebase Folders for TOP 1% AI Coding.” Here, you’ll find clear answers to the most common and important questions about agentic coding workflows, the role of structured directories, and the shift from traditional AI code generation to a system that maximizes both scale and autonomy. Whether you’re just getting started or you’re an expert seeking advanced insights, this resource aims to make agentic coding actionable and effective for business professionals and engineers alike.

What is the core idea behind using specific directories in an agentic coding workflow?

Context is everything for AI coding tools. By organizing critical information within specific directories, you give your AI agent the necessary context to understand your codebase, access documentation, define tasks, and use reusable prompts efficiently.
This structured approach ensures that the AI can work quickly and accurately, allowing engineers to scale their impact significantly.

What are the three essential directories recommended for agentic coding, and what is the purpose of each?

The three essential directories are:
AI docs: Serves as a persistent knowledge base for your AI tools,store docs, API info, code patterns, and integration details.
Specs: This is where you define plans and requirements (like PRDs) for the work you want your AI tool to perform.
.claude (or similar): Contains reusable, runnable prompts (e.g., context priming commands) that speed up onboarding and maintain consistency across coding sessions.

How does the AI docs directory function as a persistent memory for AI coding tools?

The AI docs directory acts as a dedicated knowledge repository that your AI tools can access instantly. Storing documentation,like third-party API info or unique codebase conventions,means your AI always has a reliable source to refer to, even if the context window runs out or new instances are spun up.
This is especially useful when onboarding new engineers or switching between projects.

Why is the Specs directory considered the most important folder in an agentic codebase?

The Specs directory is where you translate outcomes into detailed plans for the AI. Instead of prompting for every small task, you consolidate requirements into comprehensive specs.
This enables the AI to execute large chunks of work autonomously, often including self-validation. The planning-first mindset drives productivity and scales your engineering "compute" dramatically.

How does the concept of reusable prompts, stored in directories like .claude, enhance the agentic coding workflow?

Reusable prompts allow you to standardize and quickly execute common tasks, like context priming or running setup routines. By storing these prompts in a dedicated directory, you save time, reduce errors, and maintain consistent context for the AI across projects.
For example, a reusable "context priming" prompt makes onboarding new projects seamless and ensures the AI always starts with the right information.

What is "agentic coding," and how do these directories support it compared to traditional AI coding?

Agentic coding is a superset of AI coding. Traditional AI coding focuses on code generation from prompts, while agentic coding orchestrates broader engineering tasks,planning, building, testing, and more.
The three directories create the structure needed for this: AI docs (knowledge), Specs (plans), and reusable prompts (.claude) enable the AI to operate with greater autonomy, performing complex workflows end-to-end.

How can "plan drafting" with an AI coding tool be used in conjunction with the Specs directory?

"Plan drafting" leverages the AI's ability to quickly read and understand the codebase to create an initial plan in the Specs directory. The AI drafts the spec, and the engineer reviews, edits, and finalizes it.
This hybrid approach combines the AI's speed with human judgment, making the planning process much faster while retaining accuracy and intent.

How important is context priming, and how can it be made more efficient using these directories?

Context priming is critical for ensuring your AI coding tool understands the project. Without it, the AI risks misunderstandings and mistakes. Storing a predefined context priming prompt in your reusable prompts directory allows you to set up the AI with essential information in seconds.
This avoids redundant manual steps and ensures every session begins with the right context, whether working on a new feature or revisiting an old project.

What is the primary purpose of the AI docs directory?

The AI docs directory serves as a persistent memory and knowledge base for your AI coding tools. It stores essential documentation, codebase-specific information, integration notes, and other resources the AI may need to reference during its work.
This approach allows the AI to access relevant information instantly, reducing time spent searching and decreasing onboarding friction for new engineers.

Why is "context is king" a critical concept in generative AI engineering?

The quality and relevance of context directly impact an AI agent’s ability to deliver accurate, useful results. If the AI lacks the right context, it may produce irrelevant or incorrect output.
Structured directories ensure the AI always has access to the most important information, making engineering workflows efficient and reliable.

What kind of documents or information should you typically store in the Specs directory?

Store detailed plans, specifications, PRDs (Product Requirements Documents), and comprehensive outlines for features, tasks, or bug fixes in the Specs directory.
Think of it as the single source of truth for what needs to be built or changed. The clearer and more thorough these documents are, the better your AI tool can execute complex work.

What is the difference between AI coding and agentic coding?

AI coding is about generating code from prompts, typically focusing on small, isolated tasks.
Agentic coding goes further,it enables the AI to plan, build, test, and validate solutions, often running workflows that mirror a human engineer's full process. This shift enables bigger, more autonomous changes with less manual intervention.

What is the main benefit of using a directory like .claude (or an equivalent) for reusable prompts?

A reusable prompts directory saves time and ensures consistency. By storing frequently used prompts,like context priming or setup commands,you avoid repetitive manual steps and make your AI workflows more reliable across multiple projects and sessions.
This also helps new team members ramp up quickly.

What is the most important prompt recommended for inclusion in a reusable prompts directory?

The context priming prompt is the most important. It quickly sets up the AI coding tool’s understanding of the current codebase by reading key files (like a README) or running commands that outline the project structure.
This prompt ensures every session starts with the right context.

Describe the technique of "plan drafting" as presented in the course.

Plan drafting means letting the AI create the first version of a plan in the Specs directory. The engineer then reviews, edits, and signs off before execution.
This leverages the AI’s fast comprehension of the codebase, while the human ensures quality and alignment with business goals.

Why does the course emphasize writing a detailed plan before handing off work to an agentic tool?

Detailed plans reduce the need for iterative prompting and back-and-forth. By specifying everything the AI needs to know in one place, you enable it to execute big tasks efficiently and correctly.
This approach dramatically increases productivity and minimizes misunderstandings or rework.

How does using these three directories help scale engineering work?

The directories provide structured context (AI docs), enable large-scale task handoff (Specs), and automate repetitive steps (reusable prompts).
By systematizing your workflow, you can multiply output without multiplying manual effort. Teams can implement complex features or changes with fewer bottlenecks.

What is self-validation in the context of agentic coding, and why is it important?

Self-validation means instructing the AI tool to test its own work as part of the workflow.
This increases confidence in the results, reduces the need for constant human checking, and accelerates the development cycle by catching issues early.

What are common misconceptions about using these directories for AI coding?

One misconception is that more context is always better. In reality, overloading the AI with too much or irrelevant information can lead to confusion and lower output quality.
Another misconception is that these directories are only useful for large teams or advanced users,actually, even solo developers benefit from this structure.

How do you balance providing enough context without overwhelming the AI?

Be precise and intentional with what you include. Focus on the most relevant documentation, specs, and prompts for the specific task at hand.
For example, only include API docs or integration notes that the current feature or bug fix touches. Less is often more when it comes to effective context priming.

What is the recommended structure for the Specs directory?

Organize the Specs directory by feature, task, or project. Use clear filenames and subfolders if necessary.
For example, use specs/new-login-flow.md or specs/feature/payment-gateway/prd.md. Consistency in naming and structure makes it easy for both AI and humans to find and reference plans.

Can you give a practical example of what belongs in the AI docs directory?

Store third-party API documentation, integration notes, code conventions, and implementation patterns.
For instance, if your project integrates with Stripe, place the relevant Stripe API docs in ai-docs/stripe-api.md so the AI can access payment processing info without searching the web.

What are some examples of reusable prompts besides context priming?

Reusable prompts can include commands for code refactoring, running specific tests, generating documentation, or onboarding new contributors.
For example, a prompt like "Update all dependencies to latest versions" or "Run full regression test suite" can be stored and triggered as needed.

How do these directories help onboard new team members or AI agents?

A well-structured set of directories acts as a clear roadmap for both new engineers and AI agents.
New contributors can quickly access key documentation, understand current specs, and run context priming prompts to get up to speed,minimizing ramp-up time.

What does "the plan is the prompt" mean in agentic coding?

This phrase means the quality and detail of your plan (spec) directly determines the quality of your AI’s output.
Instead of nudging the AI with many prompts, you write one thorough plan,the AI uses it as the source of truth for execution.

When is the AI docs directory most important compared to the Specs directory?

AI docs shine during initial setup, onboarding, or whenever the AI needs to reference external information,like APIs or integration requirements.
Specs are most important during feature development or bug fixing, where detailed plans and requirements guide the AI’s work. Both are essential, but their importance shifts depending on the project phase.

How could reusable prompts directories be extended for non-coding AI tasks?

Reusable prompts can be adapted for tasks like generating reports, creating presentations, or summarizing meeting notes.
For example, a sales team might save a prompt for drafting follow-up emails, or HR could have a prompt for onboarding checklists. Any repeatable workflow benefits from this approach.

How do these directories help with the context window limitations of AI models?

By organizing information into focused directories, you control exactly what information the AI receives.
This helps you stay within context window limits while ensuring the most important details are prioritized. Reusable prompts also automate the selection and delivery of relevant context.

Are these directory practices only for Claude Code, or can they be used with other AI tools?

The principles are tool-agnostic. While ".claude" references Claude Code, any AI coding tool,such as Copilot, ChatGPT, or custom solutions,can benefit from structured directories for docs, specs, and reusable prompts.
Adapting the naming and integration points is easy.

How does this workflow change the engineer’s role?

The engineer shifts from manual coder to curator, reviewer, and planner.
You spend less time on implementation details and more time ensuring that plans and context are clear for the AI, reviewing output, and making strategic decisions.

How is this agentic coding workflow used in real-world business projects?

Businesses use agentic coding to speed up feature development, automate testing, and maintain large codebases.
For example, a fintech company might use these directories to roll out compliance updates quickly, while a SaaS startup could automate bug fixes and user-requested features by defining clear specs and leveraging reusable prompts.

What are common challenges or pitfalls when implementing this workflow?

The biggest challenges are overloading the AI with irrelevant context, under-specifying plans, and neglecting to update documentation.
Stay disciplined about what you include in each directory. Regularly review and prune docs and prompts for relevance and accuracy.

How can you measure the success of adopting agentic coding with these directories?

Track metrics like task completion speed, bug rates, onboarding time for new engineers, and the number of features shipped per cycle.
Teams often see a noticeable increase in throughput and fewer context-related errors when the workflow is implemented correctly.

Are there security or privacy considerations with storing documentation and prompts in these directories?

Yes,never store sensitive credentials, keys, or proprietary data in shared directories.
Use secure access controls, encryption, and follow your organization’s best practices for documentation. Treat these directories the same way you would treat critical codebase files.

How often should you update the AI docs and Specs directories?

Update AI docs whenever APIs change, new integrations are added, or conventions evolve.
Update Specs with each new feature, bug fix, or major project milestone. Regular maintenance keeps the AI and team operating with the most current information.

How do the Specs and AI docs directories work together during a project?

The Specs directory defines what to build, while AI docs provide the background knowledge required for implementation.
For example, a spec may reference an API documented in AI docs, giving the AI all it needs to execute the task with minimal outside input.

Can you give an example of self-validation in agentic coding?

A self-validation step might instruct the AI to run automated tests after implementing a new feature.
If the tests pass, the AI can report success; if not, it can attempt fixes or flag issues for human review. This reduces manual QA effort and builds trust in AI-driven changes.

How flexible is the directory structure? Can it be adapted for different teams or projects?

The structure is highly adaptable,customize directory names, add subfolders, or expand with additional resource types as needed.
The core idea is to keep context organized, accessible, and relevant, regardless of team size or project complexity.

How do you handle context priming across multiple coding sessions or projects?

Store multiple context priming prompts tailored for different projects, workflows, or team roles.
For example, have one prompt for onboarding, another for feature development, and a third for bug triage,each optimized for the necessary context.

Is it possible to automate the management of these directories?

Yes,many teams automate docs synchronization, spec generation, or prompt updates using scripts or workflow tools.
For example, you can trigger a context priming prompt automatically after a new branch is created or sync API docs from external sources on a schedule.

How can solo developers benefit from these directories?

Solo developers gain clarity, avoid context switching mistakes, and speed up repetitive workflows.
A well-organized AI docs folder keeps integration details handy; specs help clarify goals; reusable prompts save time on setup and routine tasks.

What is the main business value of adopting this agentic coding workflow?

This workflow increases team velocity, reduces errors, and makes engineering output more predictable.
By systematizing context and planning, businesses can deliver features faster, respond to customer needs quickly, and reduce onboarding and training costs.