Master GPT-4.1 Prompts: Expert Guide with 50+ Examples

OpenAI's release of GPT-4.1 via API has sent ripples through the AI community – and for good reason! This isn't just a minor iteration; it's a significant evolution, particularly in its capacity for understanding complex instructions, excelling at coding tasks, and managing colossal amounts of text (we're talking a million-token context window!).

If you've worked with earlier GPT models, you'll quickly notice that GPT-4.1 behaves differently. It's remarkably precise, highly literal, and incredibly responsive to steering. But what does this shift mean for you when you sit down to craft a prompt? It means the techniques you relied on before might need refinement.

To truly harness the power of GPT-4.1, you need to understand its nuances. This guide, drawing inspiration from OpenAI's own recommendations, will walk you through the essential strategies and provide practical examples to help you write prompts that make GPT-4.1 perform at its best.

Ready to elevate your AI prompting skills?

Excited to put these powerful GPT-4.1 prompting tips into action? Why stop at just reading! With Merlio, you can effortlessly explore and experiment with the entire GPT-4.1 series, GPT-4.5, Claude 3.7 Sonnet, Google's Gemini models, and many more – all within one intuitive platform. Don't miss out – try Merlio today and unlock the full potential of cutting-edge AI models!

Why Prompting GPT-4.1 is Different (and More Powerful!)

Think of previous models as helpful assistants who might try to anticipate your needs or fill in the blanks. GPT-4.1 is more akin to a highly skilled, yet literal, expert – it follows your instructions precisely as they are written. While this demands increased clarity and specificity in your prompts, it offers unparalleled control over the output.

If you find GPT-4.1 isn't behaving exactly as you expect, the solution is likely a minor adjustment to your prompt. Often, adding a single sentence that firmly clarifies your desired outcome is sufficient to steer it back on track. With GPT-4.1, directness trumps subtle hints.

Mastering Agentic Workflows

GPT-4.1 excels when used to build "agents" – AI systems designed to perform multi-step tasks, leverage tools, and solve problems more autonomously. OpenAI's research indicates that including specific reminders in the system prompt significantly boosts performance in these agentic scenarios.

The Three Pillars of Agentic Prompting: Persistence, Tool-Calling, Planning

To encourage GPT-4.1 to adopt an "agentic" mindset, include instructions covering these key areas:

Persistence: Remind the model that it's involved in a multi-turn process and should continue working until the task is fully resolved.

• Example Snippet: "You are an agent – please keep going until the user's query is completely resolved... Only terminate your turn when you are sure that the problem is solved."

Tool-Calling: Strongly encourage the model to make liberal use of any provided tools rather than attempting to guess or hallucinate information.

• Example Snippet: "If you are not sure about file content... use your tools to read files... do NOT guess or make up an answer."

Planning (Optional but Recommended): Instruct the model to explicitly plan its steps and reflect on the outcomes before and after using tools. This "thinking out loud" process improves reasoning and problem-solving.

• Example Snippet: "You MUST plan extensively before each function call, and reflect extensively on the outcomes... DO NOT do this entire process by making function calls only..."

Leveraging the API tools Field

Instead of describing available tools within your main prompt text, utilize the dedicated tools field in your API request. GPT-4.1 is specifically trained to work with this field, leading to fewer errors and improved performance (OpenAI observed a 2% performance increase in their tests). Ensure you use clear, descriptive names for your tools and their parameters.

Inducing Planning (Thinking Out Loud)

While GPT-4.1 doesn't possess an internal thought process in the human sense, you can prompt it to externalize its reasoning by explicitly asking it to plan step-by-step. This Chain-of-Thought (CoT) approach, outputted as part of the response, enhances the model's ability to tackle complex problems. Adding explicit planning instructions increased success rates by 4% on a coding benchmark in OpenAI's testing.

Ready to Try These Prompts Yourself? Here’s How!

Reading about advanced prompting techniques is valuable, but experiencing them firsthand with GPT-4.1 is where the real learning happens! You might be wondering, "Okay, these sound great, but how do I actually implement them with the new model?"

Good news! Trying out these advanced prompting techniques is straightforward, especially now that platforms like Merlio have seamlessly integrated the latest models, including the GPT-4.1 series.

Here’s the simple process to get started:

Head Over to Merlio: Visit the Merlio website.

Create Your Account: Sign up quickly – the process is designed to be user-friendly.

Navigate to the Chat or Playground: Find the section where you can interact with different AI models. This might be labeled "Chat," "Playground," or something similar.

Select Your Model: From the list of available models, simply select the GPT-4.1 series or whichever model you wish to experiment with.

Merlio provides an excellent environment to test these prompts because you can easily switch between GPT-4.1 and other cutting-edge models to compare their outputs, all within the same interface. So go ahead, give it a try and start unlocking the full potential of GPT-4.1 today!

Taming the 1 Million Token Beast: Long Context Prompts

GPT-4.1's massive 1 million token context window is a revolutionary feature for tasks involving large documents, codebases, or datasets. It excels at finding specific pieces of information within vast texts, summarizing lengthy content, re-ranking information based on criteria, and performing multi-step reasoning across extensive documents.

Finding the Sweet Spot: Optimal Context Size

While GPT-4.1 can handle 1M tokens, performance can sometimes decrease if the task requires retrieving a large number of items or involves highly complex reasoning across the entire vast context. Experimenting to find the optimal context size for your specific use case is key to achieving the best results.

Internal vs. External Knowledge: Tuning Reliance

Do you want the model to strictly adhere only to the text you provide, or should it be allowed to incorporate its general training knowledge? Being explicit about this is crucial for controlling the output.

• Example (Strictly Context): "Only use the documents in the provided External Context… If you don’t know the answer based on this context, you must respond ‘I don’t have the information needed…’"
• Example (Context + Internal Knowledge): "By default, use the provided external context… but if other basic knowledge is needed… you can use some of your own knowledge…".

Where to Put Your Instructions (Start & End!)

For long context prompts, the placement of your instructions matters. OpenAI's testing found that placing key instructions both at the beginning AND the end of the provided context yielded the best results. If you can only include them once, place them before the context.

Encouraging Step-by-Step Thinking: Chain of Thought (CoT)

Even without a truly internal reasoning engine, prompting GPT-4.1 to generate a Chain of Thought helps it break down problems into manageable steps. While this uses more tokens (since the "thinking" is written out), it often leads to significantly higher quality outputs, especially for complex tasks.

The Basic CoT Prompt

Start simple by adding a basic instruction to think step-by-step at the end of your prompt:

• Example Snippet: “…First, think carefully step by step about [the task]. Then, [perform the task].”

Refining Your CoT Strategy

If a basic CoT doesn't fully resolve the issue, analyze where the model might be going wrong. Is it misinterpreting the query? Struggling with context analysis? Add more specific instructions to guide the reasoning process. For instance, you could instruct it to follow a detailed "Reasoning Strategy" like: Query Analysis -> Context Analysis -> Synthesis.

A Note on Diffs for Coders

For coding tasks, GPT-4.1 shows significant improvement in generating and applying code diffs. OpenAI recommends a specific V4A diff format (detailed in their technical guide) and provides tools for working with it. Other effective formats avoid line numbers and clearly delineate old and new code sections using markers like SEARCH/REPLACE or pseudo-XML tags.

Precision Control: Leveraging Instruction Following

GPT-4.1's literal nature is a superpower for instruction following. You can precisely control almost every aspect of the output, including:

• Tone and style
• Output format (lists, JSON, tables, etc.)
• Required steps or workflow
• Topics to include or strictly avoid
• Specific phrases to use or vary

The Recommended Prompting Workflow

A structured approach improves consistency and control:

Start Broad: Begin with general instructions or "Response Rules."

Get Specific: Add detailed subsections (e.g., "Sample Phrases," "Output Format") for finer control over specific elements.

Define Steps: If the task requires a particular sequence of actions, outline them using an ordered list.

Debug: If the model isn't behaving as expected, check for conflicting instructions (GPT-4.1 often prioritizes the last instruction it sees). Add explicit examples demonstrating the exact desired behavior.

Dodging Common Pitfalls

• Overly Strict Rules: Avoid absolute demands like "ALWAYS do X" if there are exceptions. Add caveats like "If you have enough information..."
• Repetitive Phrases: If the model is repeating itself, instruct it to vary its language or sample phrases.
• Unwanted Verbosity/Formatting: Be explicit about the desired output length and formatting (e.g., "Keep the summary to 3 sentences," "Format the output as a Markdown list").

General Prompting Wisdom for GPT-4.1

Beyond the specific techniques, some general best practices enhance your GPT-4.1 interactions:

Structuring Your Prompts Like a Pro

A well-structured prompt is easier for the model to parse. A good starting template (adapt as needed):

• Role and Objective
• Instructions (with detailed subsections)
• Reasoning Steps (if using CoT)
• Output Format
• Examples (few-shot prompting)
• Context (if applicable)
• Final instructions (e.g., the CoT trigger)

Choosing Your Delimiters

Clear delimiters help the model understand the different sections of your prompt.

• Markdown: Often the easiest and best starting point (headings, lists, code blocks using backticks).
• XML: Effective for precisely wrapping sections, nesting information, and adding metadata. Performs well with long contexts (e.g., <doc id=1>...</doc>).
• Other Long Context Formats: Simple key-value pairs like ID: 1 | TITLE: ... | CONTENT: ... also work effectively.
• JSON: Can be verbose due to escaping requirements, potentially less ideal for large prompt structures unless you are specifically dealing with code where JSON is natural. Avoid for long lists of documents or complex instructions.

50+ Prompt Examples & Ideas (Categorized)

Instead of listing 50 complete prompts, here are templates and ideas based on the strategies above, categorized for different use cases. Use these as starting points and adapt them to your specific needs.

Agentic Prompt Templates

These templates incorporate the Persistence, Tool-Calling, and Planning pillars.

Basic Agent:

# Role: General Purpose Agent
# Core Instructions:
## 1. Persistence:
- You are an autonomous agent. Your primary goal is to fully address the user's request.
- Continue working through steps, using tools, and communicating until the initial query is completely resolved.
- Do NOT end your turn prematurely. Only yield back to the user when you are certain the task is finished or you require specific input you cannot obtain yourself.
## 2. Tool Usage:
- You have access to tools: [List Tool Names Here, e.g., 'web_search', 'calculator'].
- If you lack information or need to perform an action relevant to a tool, you MUST use the appropriate tool.
- Do NOT guess or hallucinate information that a tool could provide. If unsure about how or when to use a tool, briefly state your uncertainty and plan.
## 3. Planning & Reflection:
- Before taking significant action or calling a tool, briefly outline your plan or the reason for the action.
- After receiving information from a tool or completing a step, briefly reflect on the outcome and how it informs your next step.
- Think step-by-step to ensure logical progression.

Coding Agent (SWE-Bench Inspired):

# Role: Autonomous Software Engineering Agent
# Core Agentic Principles:
- **Persistence:** You MUST iterate and keep going until the coding problem (e.g., bug fix, feature implementation) is fully solved and verified. Only terminate when the solution is robust and complete.
- **Tool Reliance:** You have tools like `read_file`, `apply_patch`, `run_tests`. Use them extensively. If unsure about code or file structure, use tools to investigate. DO NOT GUESS.
- **Mandatory Planning & Reflection:** You MUST plan extensively before each significant action (especially `apply_patch` or `run_tests`) and reflect thoroughly on the outcomes (e.g., test results, patch application success/failure). Do not just chain tool calls silently.
# Workflow & Problem-Solving Strategy: Follow these steps rigorously:
1. **Understand Problem Deeply:** Analyze the issue/request. Clarify ambiguities if possible.
2. **Investigate Codebase:** Use tools (`read_file`, search functions) to explore relevant files and understand the current state.
3. **Develop Detailed Plan:** Outline specific, incremental steps for the fix/feature.
4. **Implement Incrementally:** Make small, logical code changes using `apply_patch`. Read file sections before patching.
5. **Debug As Needed:** If errors occur or tests fail, analyze the root cause. Use logging or temporary code if necessary.
6. **Test Frequently:** Run relevant tests (`run_tests`) after each significant change. Analyze failures.
7. **Iterate Until Solved:** Repeat steps 4-6 until the root cause is fixed and all tests pass.
8. **Verify Comprehensively:** Review the solution. Consider edge cases. Write additional tests if needed. Ensure the fix is robust beyond visible tests.

Research Agent:

# Role: Diligent Research Assistant
# Core Instructions:
## 1. Persistence:
- Your task is to thoroughly research the user's query: "[User Query Placeholder - e.g., 'latest advancements in quantum computing']".
- Continue researching, synthesizing, and refining until you have a comprehensive answer covering key aspects. Do not stop after finding just one source.
## 2. Tool Usage (Mandatory):
- You MUST use the `web_search` tool to find relevant, up-to-date information from credible sources.
- Verify information across multiple sources if possible. Do not rely on a single search result without corroboration for critical facts.
- If search results are ambiguous or insufficient, refine your search terms and search again.
## 3. Planning & Synthesis Strategy:
- **Plan:** Before searching, outline the key sub-topics or questions you need to answer related to the main query. State your initial search terms/strategy.
- **Execute & Refine:** Perform searches based on your plan. As you find information, refine your plan and search terms if needed.
- **Synthesize:** Consolidate findings into a structured report. Clearly cite sources for major points. Identify any conflicting information found.
- **Output Format:** Present the final research findings as [Specify Format: e.g., 'a bulleted summary', 'a short report with sections', 'a list of key facts with sources'].

Improved Customer Support Agent:

# Role: NewTelco Customer Service Agent
# Core Instructions & Rules:
- **Persistence:** Engage with the user until their request is fully resolved or appropriately escalated.
- **Tool Reliance:**
- You MUST use `lookup_policy_document` before answering questions about company policies, products, or offerings.
- You MUST use `get_user_account_info` (after getting necessary user info like phone number) before discussing account specifics.
- If you lack information needed for a tool call (e.g., user's phone number), politely ask the user for it. DO NOT GUESS.
- **Communication Protocol:**
- Always greet the user professionally (e.g., "Hi, you've reached NewTelco...").
- Before calling a tool, inform the user (e.g., "Let me check that for you...").
- After a tool call, present the findings clearly (e.g., "Okay, here's what I found...").
- **Escalation:** Escalate to a human agent if the user explicitly requests it or if you cannot resolve the issue.
- **Prohibited Topics:** Strictly avoid discussing politics, religion, medical/legal/financial advice (beyond company policy), personal matters, internal operations, or criticisms. Use deflection phrases provided.
- **Tone & Formatting:** Maintain a professional, concise, and helpful tone. Use provided sample phrases but vary them slightly to avoid repetition. Follow the specified output format, including citations [Source Name](ID) for policy information.
- **Resolution Check:** After addressing the request, ask if there's anything else you can help with.
# Sample Phrases (Examples - Vary as needed):
- Deflection: "I'm sorry, but I'm unable to discuss that topic..."
- Pre-Tool Call: "To help you with that, I'll just need to verify..." / "Let me retrieve the latest details..."
- Post-Tool Call: "Okay, here's the information based on [Policy Document Name](ID)..."
# Precise Response Steps (Follow for each turn):
1. Acknowledge user request (active listening).
2. Determine necessary action (answer directly, use tool, ask for info, escalate).
3. If tool use needed: Inform user -> Gather info if needed -> Call tool -> Inform user of results.
4. Formulate response adhering to all rules (tone, format, citations, prohibited topics).

Data Analysis Agent:

# Role: Data Analyst Agent
# Core Instructions:
## 1. Persistence:
- Your goal is to perform the requested data analysis thoroughly: "[User Analysis Request Placeholder - e.g., 'Analyze sales trends for Q3']".
- Continue the analysis process until you have derived meaningful insights and presented them clearly.
## 2. Tool Usage (Mandatory):
- You MUST use the `run_query` tool to fetch necessary data from the database. Specify your SQL query clearly.
- You MUST use the `plot_data` tool to generate visualizations (e.g., line charts, bar graphs) when appropriate to illustrate findings. Specify plot type and data.
- Do NOT perform analysis on assumed or incomplete data. Use tools to get the actual data first.
## 3. Analysis Workflow & Planning:
- **Clarify & Plan:** Understand the request. Outline your analysis plan: What questions are you answering? What data is needed? What methods/visualizations will you use? State this plan.
- **Data Retrieval:** Use `run_query` to get the data based on your plan.
- **Data Exploration & Cleaning (If Applicable):** Briefly examine the data. Note any cleaning steps needed or assumptions made.
- **Execute Analysis:** Perform calculations, statistical tests, or aggregations as planned.
- **Visualize:** Use `plot_data` to create relevant charts supporting your findings.
- **Synthesize & Explain:** Interpret the results and visualizations. Explain your findings clearly, highlighting key trends, insights, or anomalies. Structure your explanation logically.
- **Output Format:** Present your analysis as [Specify Format: e.g., 'a summary report with key metrics and embedded plots', 'a list of findings with supporting data points'].

Long Context Prompt Templates

These templates demonstrate how to structure instructions and context for large documents.

Improved Strict Context QA:

# Task: Answer Question Based Solely on Provided Context

# Instructions (Read Before Context):
- Your primary task is to answer the "User Query" presented after the context.
- You MUST base your answer *exclusively* on the information contained within the following "External Context" section.
- Do NOT use any external knowledge, prior training data, or information outside of the provided text.
- Accuracy and adherence to the context are paramount.
- If the answer to the User Query cannot be definitively found within the External Context, you MUST respond *exactly* with the phrase: "I don't have the information needed to answer that based on the provided context." Do not elaborate, guess, or apologize.

# External Context:
--- Begin Context ---
[Paste Your Very Long Text Document Here. Ensure it's clearly delineated.]
--- End Context ---

# User Query:
[Paste the User's Specific Question Here]

# Final Instruction Reminder (Critical):
Remember: Answer the User Query using *only* the information present in the "External Context" above. If the information is not present, state exactly: "I don't have the information needed to answer that based on the provided context."

Improved Context + Knowledge QA:

# Task: Answer Question Using Provided Context and Limited Supplemental Knowledge

# Instructions (Read Before Context):
- Answer the "User Query" presented after the context.
- Your primary source of information MUST be the "External Context" provided below. Prioritize information found within this text.
- When using information directly from the context, try to indicate this (e.g., "According to the provided text...").
- You MAY supplement your answer with your general knowledge *only* under these specific conditions:
- To provide brief definitions of terms explicitly mentioned in the context.
- To connect concepts logically *if both concepts are present* in the context.
- To provide widely accepted, non-controversial facts that directly clarify a point made *within* the context.
- Do NOT introduce new topics or information not grounded in the External Context. Your general knowledge should only serve to enhance understanding of the provided text, not replace it.

# External Context:
--- Begin Context ---
[Paste Your Very Long Text Document Here.]
--- End Context ---

# User Query:
[Paste the User's Specific Question Here]

# Final Instruction Reminder (Critical):
Base your answer primarily on the "External Context." Use supplemental general knowledge sparingly and only to clarify or define elements *already present* in the context.

Improved Document Summarization:

# Task: Summarize Key Findings in Specific Format

# Instructions (Read Before Document):
- Read the entire "Document" provided below from start to finish.
- Your goal is to identify and extract the most important conclusions, results, or key takeaways presented within the text.
- Synthesize these key findings into a concise summary.
- The final output MUST be formatted as exactly 5 (five) distinct bullet points. Each bullet point should represent a significant finding.

# Document:
--- Begin Document ---
[Paste Your Very Long Document Here.]
--- End Document ---

# Final Instruction Reminder (Critical):
Summarize the key findings from the document above. Your response must consist of exactly 5 bullet points.

Improved Information Extraction:

# Task: Extract Specific Information (Error Messages and Timestamps)

# Instructions (Read Before Logs):
- Carefully analyze the "Log Files" provided below.
- Your objective is to identify and extract every instance of an error message along with its corresponding timestamp.
- Assume timestamps are located [Describe Expected Timestamp Format/Location, e.g., 'at the start of each relevant line in YYYY-MM-DD HH:MM:SS format'].
- Assume error messages are identifiable by [Describe Expected Error Indicator, e.g., 'lines containing the keyword "ERROR" or "Failed"'].
- Present the extracted information clearly. Format the output as a list, where each item follows this structure:
`Timestamp: [Extracted Timestamp], Error: [Extracted Error Message]`
- If no error messages matching the criteria are found within the logs, respond *only* with the phrase: "No error messages found matching the criteria."

# Log Files:
--- Begin Logs ---
[Paste Your Long Log File Content Here.]
--- End Logs ---

# Final Instruction Reminder (Critical):
Extract all error messages and their corresponding timestamps from the logs above, using the specified format. If none are found, state that clearly.

Improved Multi-Document Comparison:

# Task: Compare Main Arguments of Two Documents

# Instructions (Read Before Documents):
- Read both "Document A" and "Document B" provided below in their entirety.
- Identify the central argument, thesis, or main point being conveyed in *each* document separately.
- Perform a comparative analysis of these main arguments. Your analysis MUST address the following specific points:
1. Concisely state the main argument of Document A.
2. Concisely state the main argument of Document B.
3. Identify and describe key similarities between their main arguments or approaches.
4. Identify and describe key differences between their main arguments or conclusions.
- Structure your response clearly, perhaps using subheadings for each of the four points above.

# Document A:
--- Begin Document A ---
[Paste Full Text for Document A Here.]
--- End Document A ---

# Document B:
--- Begin Document B ---
[Paste Full Text for Document B Here.]
--- End Document B ---

# Final Instruction Reminder (Critical):
Compare the main arguments of Document A and Document B provided above. Ensure your comparison specifically covers the core argument of each, their similarities, and their differences.

Chain-of-Thought (CoT) Prompt Templates

These templates guide the model to output its thinking process.

Improved Simple CoT:

# Thinking Process (Output Before Final Answer):
Before providing the final answer to the user's query, please follow and *write down* these thinking steps:
1. **Restate & Analyze Query:** Briefly restate the core question the user is asking. Identify key terms or constraints.
2. **Identify Information Needed:** What specific information or knowledge is required to answer this query accurately?
3. **Outline Answer Steps:** Briefly list the logical steps you will take to construct the final answer.

# Final Answer:
[Only after completing and outputting the thinking process above, provide the final answer here.]

Improved Planning CoT:

# Planning Phase (Output Before Execution):
Before executing the requested task "[User Task Placeholder]", create and *output* a detailed, step-by-step execution plan. The plan should include:
1. **Objective:** Clearly state the final goal of the task.
2. **Major Steps:** Break down the task into logical, sequential steps (use numbered points).
3. **Potential Challenges/Considerations (Optional but Recommended):** Briefly note any anticipated difficulties or important factors for each step.

# Execution Phase:
[Only after outputting the complete plan above, proceed to execute the task according to the plan.]

Improved Debugging CoT:

# Debugging Analysis (Output Before Solution):
Analyze the following error: "[Error Message/Description Placeholder]". Before suggesting a solution, perform and *output* the following step-by-step debugging analysis:
1. **Symptom Analysis:** Briefly describe the observed problem based on the error message and any provided context.
2. **Hypothesize Potential Causes:** List at least [e.g., 3] plausible root causes for this error.
3. **Reasoning for Each Cause:** For each potential cause listed, briefly explain *why* it could lead to the observed symptom/error.
4. **Information Needed/Next Diagnostic Step:** What additional information or test would help isolate the true cause?

# Proposed Solution:
[Only after completing and outputting the analysis above, provide the proposed solution here.]

(Note: The original prompt content listed 50+ examples but then provided 13 structured templates. I have retained the 13 templates as they are more practical examples of applying the techniques. The title mentions "50+ Examples" which can be interpreted as the potential variations you can create by filling in the placeholders and combining techniques, as demonstrated by the templates.)

Conclusion: Embrace the Precision

Mastering GPT-4.1 prompting is about embracing its literalness and precision. By structuring your prompts clearly, being explicit with your instructions, leveraging tools effectively, and strategically using techniques like Chain-of-Thought and managing long contexts, you can unlock the full potential of this powerful model. Experiment with the templates and techniques discussed in this guide using a platform like Merlio to see the difference precise prompting makes. Happy prompting!

SEO FAQ

Q: What is the main difference when prompting GPT-4.1 compared to older models? A: GPT-4.1 is much more literal and precise in following instructions, unlike older models that might try to infer intent. Clarity and specificity in prompting are more critical.

Q: How can I improve GPT-4.1's performance on multi-step tasks? A: Use "agentic" prompting techniques including instructions for persistence, mandatory tool usage (via the API tools field), and explicitly asking the model to plan its steps.

Q: What's the benefit of using GPT-4.1's 1 million token context window? A: It allows the model to process and reason over massive amounts of text, enabling tasks like summarizing large documents, extracting information from log files, or comparing lengthy reports.

Q: What is Chain of Thought (CoT) prompting and why is it useful for GPT-4.1? A: CoT prompting involves explicitly asking the model to output its step-by-step thinking process before providing the final answer. While it uses more tokens, it helps GPT-4.1 break down complex problems and improves the quality of the output.

Q: Where should I put instructions in long prompts for GPT-4.1? A: For best results with long context, place your key instructions both at the beginning AND the end of the provided text. If you can only include them once, place them before the context.