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AI Prompts for Writing, Analysis, Coding & Research
Select a category or browse all Meta Llama prompts below
1. Llama Multimodal Image Analyst
MULTIMODALAnalyze an image and extract useful visual information.
Analyze the attached image carefully. Identify the important objects,
text, layout, visual patterns, and relevant details. Separate what
you can directly observe from anything you are inferring, then give
me the most useful conclusions.
Analyze the attached image carefully. Identify the important objects, text, layout, visual patterns, and relevant details. Separate what you can directly observe from anything you are inferring, then give me the most useful conclusions.
2. Llama Long-Context Document Analyst
ANALYSISAnalyze a large document and find the most important information.
Analyze this document: [paste document]. Create a structured summary,
identify the main arguments, important facts, contradictions, missing
information, and actionable conclusions. Reference the relevant
sections when possible.
Analyze this document: [paste document]. Create a structured summary, identify the main arguments, important facts, contradictions, missing information, and actionable conclusions. Reference the relevant sections when possible.
3. Llama Coding Agent
CODINGPlan, build, review, and improve a coding task.
Act as a senior coding assistant. Build [FEATURE/PROJECT] using
[LANGUAGE/FRAMEWORK]. First plan the implementation, then write the
code, check for errors and edge cases, and finish with testing steps
and possible improvements.
Act as a senior coding assistant. Build [FEATURE/PROJECT] using [LANGUAGE/FRAMEWORK]. First plan the implementation, then write the code, check for errors and edge cases, and finish with testing steps and possible improvements.
4. Llama Code Debugger
CODINGFind the root cause of bugs and provide a reliable fix.
Debug this code: [CODE]. Identify the root cause, explain the failure
clearly, provide the corrected version, and check for related bugs,
security issues, and edge cases before giving the final solution.
Debug this code: [CODE]. Identify the root cause, explain the failure clearly, provide the corrected version, and check for related bugs, security issues, and edge cases before giving the final solution.
5. Llama AI Research Agent
RESEARCHTurn a complex topic into a structured research workflow.
Research [TOPIC] systematically. Break the topic into key questions,
identify what evidence is needed, compare competing viewpoints, and
organize the findings into facts, uncertainties, and conclusions.
Do not present assumptions as verified facts.
Research [TOPIC] systematically. Break the topic into key questions, identify what evidence is needed, compare competing viewpoints, and organize the findings into facts, uncertainties, and conclusions. Do not present assumptions as verified facts.
6. Llama Structured Data Extractor
DATAExtract useful structured information from messy text.
Extract the following information from this text: [TEXT].
Return the results as structured JSON using these fields:
[FIELD 1, FIELD 2, FIELD 3]. Use null when information is missing
and do not invent values.
Extract the following information from this text: [TEXT]. Return the results as structured JSON using these fields: [FIELD 1, FIELD 2, FIELD 3]. Use null when information is missing and do not invent values.
7. Llama Tool-Calling Assistant
AGENTSPlan which tools or actions are needed to complete a task.
Act as a tool-using AI assistant. My goal is [GOAL]. Determine
which available tools are necessary, what information each tool
needs, the correct order of actions, and what should be verified
before reporting the final result.
Act as a tool-using AI assistant. My goal is [GOAL]. Determine which available tools are necessary, what information each tool needs, the correct order of actions, and what should be verified before reporting the final result.
8. Llama Reasoning & Decision Assistant
REASONINGBreak difficult decisions into evidence, trade-offs, and actions.
Analyze this decision: [DECISION]. Identify the key criteria,
constraints, trade-offs, risks, and unknowns. Compare the available
options and provide a recommendation based only on the information
provided. Clearly state what additional information could change it.
Analyze this decision: [DECISION]. Identify the key criteria, constraints, trade-offs, risks, and unknowns. Compare the available options and provide a recommendation based only on the information provided. Clearly state what additional information could change it.
9. Llama Multilingual Assistant
MULTILINGUALTranslate and adapt content while preserving its intended meaning.
Translate this content into [LANGUAGE] for readers in [REGION].
Preserve the original meaning, tone, formatting, and important
terminology. Adapt idioms and cultural references only when needed,
and flag any phrase that could have multiple interpretations.
Translate this content into [LANGUAGE] for readers in [REGION]. Preserve the original meaning, tone, formatting, and important terminology. Adapt idioms and cultural references only when needed, and flag any phrase that could have multiple interpretations.
10. Llama Agent Workflow Builder
AGENTSDesign a multi-step AI agent workflow for a real task.
Design an AI agent workflow for [GOAL]. Define the objective,
inputs, steps, tools, decision points, memory requirements,
verification checks, failure handling, and final output. Keep
human approval for actions that require judgment or authorization.
Design an AI agent workflow for [GOAL]. Define the objective, inputs, steps, tools, decision points, memory requirements, verification checks, failure handling, and final output. Keep human approval for actions that require judgment or authorization.
11. Bias Audit for AI Training Data Description
ANALYSISIdentify potential biases in a dataset before training.
I'm describing a dataset intended for [ML task]: [dataset description]. Analyze the description for potential sources of bias: representation, measurement, aggregation, and historical bias. Suggest concrete steps to mitigate each.
I'm describing a dataset intended for [ML task]: [dataset description]. Analyze the description for potential sources of bias: representation, measurement, aggregation, and historical bias. Suggest concrete steps to mitigate each.
12. Competitor Feature Comparison Matrix
ANALYSISBuild a detailed comparison table of competitors.
Compare [our product] with [competitor A] and [competitor B] on these dimensions: pricing, key features, target audience, support quality, and innovation. Output a markdown table and a paragraph summarizing our unique advantage.
Compare [our product] with [competitor A] and [competitor B] on these dimensions: pricing, key features, target audience, support quality, and innovation. Output a markdown table and a paragraph summarizing our unique advantage.
13. Policy Impact Forecasting
ANALYSISSimulate the effects of a new policy on different stakeholders.
Analyze the likely impacts of this proposed policy: [policy text]. Identify primary, secondary, and tertiary effects on [list stakeholders]. Provide a scenario matrix: short-term vs long-term, intended vs unintended consequences.
Analyze the likely impacts of this proposed policy: [policy text]. Identify primary, secondary, and tertiary effects on [list stakeholders]. Provide a scenario matrix: short-term vs long-term, intended vs unintended consequences.
14. Root Cause Investigation (Ishikawa Diagram)
ANALYSISUse a structured fishbone diagram approach to find root causes.
The problem is: [describe problem]. Perform a root cause analysis using the Ishikawa (fishbone) categories: People, Process, Equipment, Materials, Environment, Management. List possible causes under each and identify the top 3 most probable root causes.
The problem is: [describe problem]. Perform a root cause analysis using the Ishikawa (fishbone) categories: People, Process, Equipment, Materials, Environment, Management. List possible causes under each and identify the top 3 most probable root causes.
15. Sentiment Analysis & Trend Extraction from Text
ANALYSISAnalyze open-ended survey responses for sentiment and key themes.
Analyze these customer feedback comments: [paste comments]. First, classify each as positive, negative, or neutral. Then, extract the top 5 recurring themes. Provide a summary of overall sentiment and one actionable recommendation.
Analyze these customer feedback comments: [paste comments]. First, classify each as positive, negative, or neutral. Then, extract the top 5 recurring themes. Provide a summary of overall sentiment and one actionable recommendation.
16. Multi-Document Contradiction Finder
ANALYSISLoad multiple long documents and find inconsistencies.
I'm uploading three reports: [paste/describe docs]. Compare them and highlight any factual contradictions, conflicting statistics, or inconsistent claims. Create a table of the contradictions with exact references to each document.
I'm uploading three reports: [paste/describe docs]. Compare them and highlight any factual contradictions, conflicting statistics, or inconsistent claims. Create a table of the contradictions with exact references to each document.
17. Full API Endpoint with Validation
CODINGGenerate a production-ready API route with input validation and error handling.
Create a [Node.js/FastAPI] endpoint for [resource, e.g., user registration]. Include request body validation, password hashing, error responses in a standard format, and a success response. Use environment variables for secrets. Write clean, commented code.
Create a [Node.js/FastAPI] endpoint for [resource, e.g., user registration]. Include request body validation, password hashing, error responses in a standard format, and a success response. Use environment variables for secrets. Write clean, commented code.
18. Database Schema Design from Requirements
CODINGTranslate a product spec into a normalized database schema.
Given these requirements: [describe app requirements]. Design a PostgreSQL schema. Provide CREATE TABLE statements with appropriate data types, primary keys, foreign keys, and indexes. Justify your normalization choices and suggest any denormalization for performance if needed.
Given these requirements: [describe app requirements]. Design a PostgreSQL schema. Provide CREATE TABLE statements with appropriate data types, primary keys, foreign keys, and indexes. Justify your normalization choices and suggest any denormalization for performance if needed.
19. Algorithm Complexity Analyzer
CODINGAnalyze a code snippet's time and space complexity with improvement suggestions.
Here is a function: [paste code]. Analyze its time and space complexity in Big-O notation. Walk through how you arrived at the result. Then, propose an optimized version if possible, and compare the complexity of the two approaches.
Here is a function: [paste code]. Analyze its time and space complexity in Big-O notation. Walk through how you arrived at the result. Then, propose an optimized version if possible, and compare the complexity of the two approaches.
20. Security Vulnerability Patch
CODINGIdentify and fix security issues in code.
Review this code for security vulnerabilities: [paste code]. Focus on OWASP Top 10 risks. For each vulnerability found, explain the risk, provide a patched code snippet, and recommend security best practices to avoid similar issues.
Review this code for security vulnerabilities: [paste code]. Focus on OWASP Top 10 risks. For each vulnerability found, explain the risk, provide a patched code snippet, and recommend security best practices to avoid similar issues.
21. Dockerfile and CI/CD Pipeline Generator
CODINGGenerate a complete containerization and deployment setup.
For this project description: [describe project tech stack], generate a Dockerfile and a GitHub Actions CI/CD pipeline that builds, tests, and pushes the image to a registry. Include comments explaining each step.
For this project description: [describe project tech stack], generate a Dockerfile and a GitHub Actions CI/CD pipeline that builds, tests, and pushes the image to a registry. Include comments explaining each step.
22. Unit Test Suite for Edge Cases
CODINGGenerate thorough unit tests covering typical and edge cases.
Write unit tests using [framework, e.g., Jest] for this function: [paste function]. Cover: normal inputs, boundary values, null/undefined, empty arrays, and invalid types. Each test should have a clear description of what it verifies.
Write unit tests using [framework, e.g., Jest] for this function: [paste function]. Cover: normal inputs, boundary values, null/undefined, empty arrays, and invalid types. Each test should have a clear description of what it verifies.
23. Code Explanation for Junior Developers
CODINGExplain complex code in simple, educational terms.
Explain this code to a junior developer who is learning [language]: [paste code]. Break it down line by line, explain the purpose of each block, and highlight any tricky concepts. Use analogies where helpful.
Explain this code to a junior developer who is learning [language]: [paste code]. Break it down line by line, explain the purpose of each block, and highlight any tricky concepts. Use analogies where helpful.
24. Research Paper Abstract Generator
RESEARCHDraft a structured abstract from a full paper or notes.
Using the provided paper text: [paste paper sections], write a structured abstract (Background, Methods, Results, Conclusions) in under 300 words. Ensure the abstract accurately reflects the paper's contributions and does not overclaim.
Using the provided paper text: [paste paper sections], write a structured abstract (Background, Methods, Results, Conclusions) in under 300 words. Ensure the abstract accurately reflects the paper's contributions and does not overclaim.
25. Literature Review Gap Finder
RESEARCHIdentify research gaps from a set of paper summaries.
Here are summaries of 10 papers on [topic]: [paste summaries]. Synthesize their main findings, map out the consensus and disagreements, and specifically identify three under-researched areas that would make viable future research questions.
Here are summaries of 10 papers on [topic]: [paste summaries]. Synthesize their main findings, map out the consensus and disagreements, and specifically identify three under-researched areas that would make viable future research questions.
26. Survey Question Design Assistant
RESEARCHCreate unbiased, effective survey questions.
I need to measure [construct, e.g., customer satisfaction] in a survey. Draft 10 questions using a mix of Likert scale, multiple choice, and open-ended formats. Review the questions for leading language, double-barreling, and social desirability bias, and suggest improvements.
I need to measure [construct, e.g., customer satisfaction] in a survey. Draft 10 questions using a mix of Likert scale, multiple choice, and open-ended formats. Review the questions for leading language, double-barreling, and social desirability bias, and suggest improvements.
27. Qualitative Data Coding Assistant
RESEARCHPerform thematic coding on interview transcripts.
Code this interview transcript using thematic analysis: [paste transcript]. First, generate initial open codes. Then, group them into broader themes. Provide a summary of each theme with illustrative quotes from the transcript.
Code this interview transcript using thematic analysis: [paste transcript]. First, generate initial open codes. Then, group them into broader themes. Provide a summary of each theme with illustrative quotes from the transcript.
28. Patent Prior Art Search Query Builder
RESEARCHBuild a search strategy for patent databases.
I have an invention described as: [invention description]. Generate a Boolean search query for patent prior art, combining IPC/CPC classifications, keywords, and synonyms. Provide the logic behind the query construction.
I have an invention described as: [invention description]. Generate a Boolean search query for patent prior art, combining IPC/CPC classifications, keywords, and synonyms. Provide the logic behind the query construction.
29. Long Document Summarization with Hierarchical Outlines
RESEARCHSummarize a 100-page report into a nested, structured digest.
Summarize this long document: [paste/upload]. Create a hierarchical outline with main chapters/sections, sub-points, and a one-sentence summary for each sub-point. End with an overall executive summary.
Summarize this long document: [paste/upload]. Create a hierarchical outline with main chapters/sections, sub-points, and a one-sentence summary for each sub-point. End with an overall executive summary.
AI Prompt Templates for Meta Llama Models
These AI prompt templates are optimized for Meta's Llama family, from the efficient 8B to the powerful 405B and the upcoming Llama 4. They capitalize on Llama's open-source nature, strong reasoning, and multilingual fluency. Whether you're running locally or via API, these prompts will help you generate high-quality content, perform deep analysis, produce robust code, and accelerate research.
How Can These AI Prompts Improve My Llama Results?
These AI prompts improve Llama results by providing clear, structured instructions that align with the model's strengths. By specifying exact formats, roles, reasoning steps, and output types, you reduce ambiguity and guide the model to produce more accurate, detailed, and useful responses. The prompts also leverage Llama's long-context and multilingual capabilities, making them suitable for complex, real-world tasks.
Frequently Asked Questions
What was updated on this page for September 2026?
This page was refreshed on September 09, 2026 with updated prompt wording, cleaner formatting, and improved guidance so readers can quickly find the most useful AI prompt templates.
What are the best AI prompts for Meta Llama models in 2026?
The best AI prompts for Meta Llama in 2026 leverage the model's open-source flexibility, extended context windows, and strong reasoning. They cover writing, deep analysis, code generation, and research with detailed, structured instructions.
Do these prompts work with all Llama versions?
Yes. These prompts are designed for Llama 3.1, Llama 4, and fine-tuned variants. They use general prompt engineering principles that apply across the Llama family, though larger models may produce richer results.
Are these prompts beginner-friendly?
Absolutely. Just copy any prompt and replace the bracketed placeholders with your own details. No advanced prompt engineering knowledge is needed.
Can these prompts take advantage of Llama's long context and multilingual features?
Yes. Many prompts are built to process lengthy documents or multiple files in one go, and others support multilingual translation and localization, making full use of Llama's extended context and language capabilities.
Should I fact-check information from Llama prompts?
Yes. Llama, like any AI, can generate plausible but incorrect content. Always verify important facts, figures, or advice with trusted, up-to-date sources.
