# [OpenAI Cookbook Pro: Comprehensive GPT-4.1 Application Framework](https://chatgpt.com/canvas/shared/6825fb38b0e0819184bb3153a3eb1a52)

## Introduction

This document represents a fully evolved, professional-grade implementation of the OpenAI 4.1 Cookbook. It serves as a unified, production-ready guide for applied large language model deployment using GPT-4.1. Each section draws from OpenAI's internal best practices and external application patterns to provide a durable blueprint for advanced AI developers, architects, and researchers.

This Cookbook Pro version encapsulates:

* High-performance agentic prompting workflows
* Instruction literalism and planning strategies
* Long-context structuring methods
* Tool-calling schemas and evaluation principles
* Diff management and debugging strategies

---

## Part I — Agentic Workflows

### 1.1 Prompt Harness Configuration

#### Three Essential Prompt Reminders:

```markdown
# Persistence
You are an agent—keep working until the task is fully resolved. Do not yield control prematurely.

# Tool-Calling
If unsure about file or codebase content, use tools to gather accurate information. Do not guess.

# Planning
Before and after every function call, explicitly plan and reflect. Avoid tool-chaining without synthesis.
```

These instructions significantly increase performance and enable stateful execution in multi-message tasks.

### 1.2 Example: SWE-Bench Verified Prompt

```markdown
# Objective
Fully resolve a software bug from an open-source issue.

# Workflow
1. Understand the problem.
2. Explore relevant files.
3. Plan incremental fix steps.
4. Apply code patches.
5. Test thoroughly.
6. Reflect and iterate until all tests pass.

# Constraint
Only end the session when the problem is fully fixed and verified.
```

---

## Part II — Instruction Following & Output Control

### 2.1 Instruction Clarity Protocol

Use:

* `# Instructions`: General rules
* `## Subsections`: Detailed formatting and behavioral constraints
* Explicit instruction/response pairings

### 2.2 Sample Format

```markdown
# Instructions
- Always greet the user.
- Avoid internal knowledge for company-specific questions.
- Cite retrieved content.

# Workflow
1. Acknowledge the user.
2. Call tools before answering.
3. Reflect and respond.

# Output Format
Use: JSON with `title`, `answer`, `source` fields.
```

---

## Part III — Tool Integration and Execution

### 3.1 Schema Guidelines

Define tools via the `tools` API parameter, not inline prompt injection.

#### Tool Schema Template

```json
{
  "name": "lookup_policy_document",
  "description": "Retrieve company policy details by topic.",
  "parameters": {
    "type": "object",
    "properties": {
      "topic": {"type": "string"}
    },
    "required": ["topic"]
  }
}
```

### 3.2 Tool Usage Best Practices

* Define sample tool calls in `# Examples` sections
* Never overload the `description` field
* Validate inputs with required keys
* Prompt model to message user before and after calls

---

## Part IV — Planning and Chain-of-Thought Induction

### 4.1 Step-by-Step Prompting Pattern

```markdown
# Reasoning Strategy
1. Query breakdown
2. Context extraction
3. Document relevance ranking
4. Answer synthesis

# Instruction
Think step by step. Summarize relevant documents before answering.
```

### 4.2 Failure Mitigation Strategies

| Problem           | Fix                                         |
| ----------------- | ------------------------------------------- |
| Early response    | Add: “Don’t conclude until fully resolved.” |
| Tool guess        | Add: “Use tool or ask for missing data.”    |
| CoT inconsistency | Prompt: “Summarize findings at each step.”  |

---

## Part V — Long Context Optimization

### 5.1 Instruction Anchoring

* Repeat instructions at both top and bottom of long input
* Use structured section headers (Markdown/XML)

### 5.2 Effective Delimiters

| Type     | Example                 | Use Case           |                        |
| -------- | ----------------------- | ------------------ | ---------------------- |
| Markdown | `## Section Title`      | General purpose    |                        |
| XML      | `<doc id='1'>...</doc>` | Document ingestion |                        |
| ID/Title | \`ID: 3                 | TITLE: ...\`       | Knowledge base parsing |

### 5.3 Example Prompt

```markdown
# Instructions
Use only documents provided. Reflect every 10K tokens.

# Long Context Input
<doc id="14" title="Security Policy">...</doc>
<doc id="15" title="Update Note">...</doc>

# Final Instruction
List all relevant IDs, then synthesize a summary.
```

---

## Part VI — Diff Generation and Patch Application

### 6.1 Recommended Format: V4A Diff

```bash
*** Begin Patch
*** Update File: src/utils.py
@@ def sanitize()
-    return text
+    return text.strip()
*** End Patch
```

### 6.2 Diff Patch Execution Tool

```json
{
  "name": "apply_patch",
  "description": "Apply structured code patches to files",
  "parameters": {
    "type": "object",
    "properties": {
      "input": {"type": "string"}
    },
    "required": ["input"]
  }
}
```

### 6.3 Workflow

1. Investigate issue
2. Draft V4A patch
3. Call `apply_patch`
4. Run tests
5. Reflect

### 6.4 Edge Case Handling

| Symptom             | Action                              |
| ------------------- | ----------------------------------- |
| Incorrect placement | Add `@@ def` or class scope headers |
| Test failures       | Revise patch + rerun                |
| Silent error        | Check for malformed format          |

---

## Part VII — Output Evaluation Framework

### 7.1 Metrics to Track

| Metric                     | Description                                          |
| -------------------------- | ---------------------------------------------------- |
| Tool Call Accuracy         | Valid input usage and correct function selection     |
| Response Format Compliance | Matches expected schema (e.g., JSON)                 |
| Instruction Adherence      | Follows rules and workflow order                     |
| Plan Reflection Rate       | Frequency and quality of plan → act → reflect cycles |

### 7.2 Eval Tags for Audit

```markdown
# Eval: TOOL_USE_FAIL
# Eval: INSTRUCTION_MISINTERPRET
# Eval: OUTPUT_FORMAT_OK
```

---

## Part VIII — Unified Prompt Template

Use this as a base structure for all GPT-4.1 projects:

```markdown
# Role
You are a [role] tasked with [objective].

# Instructions
[List core rules here.]

## Response Rules
- Always use structured formatting
- Never repeat phrases verbatim

## Workflow
[Include ordered plan.]

## Reasoning Strategy
[Optional — for advanced reasoning tasks.]

# Output Format
[Specify format, e.g., JSON or Markdown.]

# Examples
## Example 1
Input: "..."
Output: {...}
```

---

## Final Notes

GPT-4.1 represents a leap forward in real-world agentic performance, tool adherence, long-context reliability, and instruction precision. However, performance hinges on prompt clarity, structured reasoning scaffolds, and modular tool integration.

To deploy GPT-4.1 at professional scale:

* Treat every prompt as a program
* Document assumptions
* Version control your system messages
* Build continuous evals for regression prevention

**Structure drives performance. Precision enables autonomy.**

Welcome to Cookbook Pro.

—End of Guide—