AI Coding Best Practices

TalkCody AI Coding workflows to improve efficiency, reduce costs, and ensure quality

This guide focuses on three goals: efficiency, cost reduction, and quality improvement. It summarizes TalkCody's key features and practical methods to help you turn AI into deliverable engineering productivity, rather than just an inspiration-based "chat tool".

AI Programming Trends in 2026

Understanding these trends will help you better grasp the future direction of AI programming:

📈 AI Agent Concurrency Leap

AI coding Agent concurrency will reach 10-20: Moving from current single-task linear execution to handling 10-20 parallel tasks simultaneously. AI coding Agents will evolve from "code generators" to Task orchestrators, responsible for coordinating multiple subtasks, agents, and tools.

🌙 New Work Paradigm: Night Coding + Day Review

Night + Weekend AI Automated Coding: Leverage non-working hours for AI to batch code, test, and generate documentation.

Daytime Review, Think, Discuss, and Critical Testing: Humans focus on high-value activities—code review, architecture decisions, technical discussions, and critical test validation. This paradigm maximizes human time value.

👤 Career Boundary Restructuring: The Maker Era

Developer, Product Manager, and Tester roles will gradually merge: AI Agents can handle coding, requirement analysis, test case generation, and multi-role work.

Only the Maker profession will remain: Future engineers won't be single "programmers" or "testers", but Makers who can harness AI Agents and manage the full process—understanding technology, products, and delivering results.

1. Model Selection: Always Use the Best

Core Principle: Time is Your Most Valuable Asset

Always use the most powerful model.

Your most valuable asset is your time, not API costs. The time saved by choosing a stronger model far exceeds the token fees saved.

Recommended Model Configuration

The following models are recommended in TalkCody:

Primary Model: GPT-5.2 Codex
- Use OpenAI Subscription in TalkCody
- Top-tier code understanding and generation capabilities
- Suitable for complex reasoning and multi-file refactoring
High-Frequency Coding Scenarios: Coding Plan Built-in Models
- Optimized for programming
- Controllable costs
- Stable and reliable performance

Model Tiering Strategy

Main Model: Complex tasks, architecture design, critical code modifications
Small Model: Simple format conversions, document generation, basic Q&A

Don't sacrifice quality to save costs. One successful task completion is far better than ten failed attempts.

2. Efficiency: Let AI Help You Run Faster

1. Use Plan Mode for Complex Tasks

For multi-file, critical changes, and complex requirements, enable Plan Mode to let AI create a plan before execution.
Approve the plan before implementation, significantly reducing rework and repeated communication costs.

2. Role-Based Collaboration: Agents + Skills

Use AI Agents to split tasks into "roles": code reviewer, test generator, document writer, etc.
Use Skills to叠加 specific domain capabilities, allowing the same agent to quickly switch between different scenarios.
Use Small Model for simple tasks, Main Model for complex reasoning, avoiding "using a sledgehammer to crack a nut".

3. Tool-Driven Positioning, Reduce Invalid Context

Proficiently use Tools and Global Search, locate first then read, avoiding "feeding the entire codebase to AI".
When latest information is needed, enable Web Search or Coding Plan built-in search to reduce errors and outdated information.

The more precise the tool usage, the cleaner the AI's context, resulting in higher inference quality and speed.

4. Parallel Processing: Worktree

When running multiple tasks in parallel, enable Worktree to let each task execute in an isolated directory, avoiding mutual overwrites.
Read-only tasks don't need Worktree, avoiding unnecessary overhead.

5. Instant Feedback: LSP + Lint + Terminal

Enable LSP and Code Lint, using "instant diagnostics" to shorten feedback loops.
Use Terminal Integration to quickly run scripts and verify commands, reducing switching costs.

3. Cost: Spend Money Where It Matters

1. Reuse Subscriptions, Prioritize Existing Quotas

Existing subscriptions can be directly reused:

2. Coding Plan for High-Frequency Coding

Coding Plan is a subscription solution for programming scenarios, suitable for high-frequency use with more controllable costs.
Once enabled, you can directly use built-in MCP search/image recognition tools, reducing additional API expenses.

3. Fully Utilize Free and Low-Cost Solutions

Refer to the Free Use Guide to choose suitable free or local model solutions.

4. Control Context Costs

Use /compact to compress conversation context, reducing token waste (see Commands).
Avoid repeatedly reading large files; prioritize search tools for positioning, then read precisely.
Understand TalkCody's context compression mechanism; refer to Context Compression Principles.

4. Quality: Turn AI into a Deliverable Engineering Process

1. Generate AGENTS.md with /init

/init generates the project collaboration specification file AGENTS.md, providing stable engineering rules for AI.
Agent dynamic prompts also read AGENTS.md, significantly improving output consistency.
See Commands for command details.

2. Use Hooks as Quality Gates

Hooks can execute commands during the task lifecycle, implementing CI-like quality interception. The project provides an example script scripts/hooks/stop-checks.ts, which sequentially executes bun run tsc, bun run test, bun run lint, and blocks task completion on failure.

Example configuration (project-level .talkcody/settings.json):

{
  "hooks": {
    "Stop": [
      {
        "matcher": "*",
        "hooks": [
          {
            "type": "command",
            "command": "bun scripts/hooks/stop-checks.ts",
            "timeout": 600,
            "description": "Run tsc/test/lint before finishing"
          }
        ]
      }
    ]
  }
}

Hooks execute commands locally; only enable them in trusted projects and set appropriate timeouts based on project scale.

3. One-Click AI Code Review

TalkCody supports one-click AI code review, letting AI act as a professional code reviewer:

Start Review

After file changes, TalkCody can automatically trigger agents for in-depth review

Multi-Dimensional Check

Code Quality: Detect code smells, complexity, potential bugs
Security: Identify security vulnerabilities, sensitive information leakage risks
Best Practices: Evaluate compliance with project coding standards and design patterns
Maintainability: Check code readability, comment completeness

Generate Report

Automatically generate structured review reports, including issue location, severity, and fix suggestions

One-Click Fix

Support one-click fix code generation based on review reports

AI Code Review transforms code review from "manual time-consuming" to "second-level response", significantly improving code quality and team efficiency.

4. Use LSP + Lint + Tests to Form a Closed Loop

LSP finds issues first, Lint reinforces standards, tests validate logic; these three form a quality closed loop.
For complex changes, recommend pairing with a "Code Review Agent" for secondary verification.

5. How to Plan Efficiently

Open-Ended vs Closed-Ended Questions

❌ Wrong: Closed-Ended Questions

"Is this solution good?" "Is this written correctly?"

These questions can only get binary answers of "good/not good", "right/wrong", unable to stimulate AI's exploration capabilities.

✅ Correct: Open-Ended Questions

"What technical solutions might AI have for this requirement? What are their respective pros and cons?"

"What are the possible implementation paths? Help me analyze the applicable scenarios for each approach."

These questions let AI actively explore multiple possibilities, providing comprehensive analysis and comparison.

Plan Mode Efficient Questioning Techniques

When using Plan Mode, it's recommended to:

Describe goals, not paths
- ✅ "We need to implement a file upload feature, supporting breakpoint resume and large file handling"
- ❌ "Use WebSocket to implement file upload"
Request multi-solution comparison
- "Please provide 2-3 technical solutions, analyzing their respective tech stacks, complexity, performance, and maintenance costs"
Clarify constraints
- "Considering our team is familiar with TypeScript and needs mobile compatibility, what solutions do you recommend?"
Request risk assessment
- "What risks might each solution have? What pitfalls need special attention?"

Open-ended questioning maximizes AI's exploration capabilities, allowing you to obtain more comprehensive and profound solution analysis.

Model Selection: Always use the best model + GPT-5.2 Codex as primary + Coding Plan for high-frequency
Efficiency: Plan Mode planning + precise tool positioning + Worktree parallelism + LSP/Lint instant feedback
Cost: Subscription reuse + Coding Plan + free solutions + /compact context control
Quality: /init standardization + Hooks quality gates + AI Code Review + test closed loop
Plan: Open-ended questions + multi-solution comparison + clear constraints + risk assessment

AI Coding Best Practices

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