Prompts with examples: when few-shot helps and when it only adds noise
Learn when examples genuinely improve a prompt, when they only add unnecessary context, and how to compare zero-shot and few-shot with a representative test set.
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AI Prompts: Context & Performance
Ready-to-use prompts and guides for getting more from AI. Learn prompt engineering, context, tokens, memory, testing, and techniques for improving response quality.
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Zero-shot vs. few-shot: when examples improve a prompt and when they waste context
Compare instructions without examples and prompts with demonstrations on the same test cases to measure format, accuracy, and token cost.
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Prompt to compare two spreadsheet versions and explain every difference without changing the data
A prompt template for comparing File A and File B in read-only mode, finding differences in structure, values, formulas, and formatting, and producing a traceable report.
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Copilot in Excel with Python: 9 prompts to clean data, test hypotheses, and build auditable analyses
Ready-to-use prompts for Python through Copilot in Excel with explicit inputs, checkpoints, validation, and an audit trail.
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Ready-to-Use Prompts4 articles
Prompts to copy, adapt and use in real tasks, accompanied by instructions, variables, examples and limitations.
Prompt Engineering5 articlesTechniques for creating, structuring, testing, and refining prompts for different AI tasks and models.
Context, Tokens and Memory4 articlesContext windows, tokens, memory, files, long conversations and ways to provide information to AI.
AI Performance3 articlesTests, comparisons and methods to improve the quality, accuracy, consistency and efficiency of responses.
AI Models and Tools1 articleHow ChatGPT, Claude, Gemini and other models differ in prompting, context, tools and behavior.
Agents and Automation3 articlesPrompts, tools, agents, workflows, and techniques to take AI beyond a single conversation.
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Recent posts and new content.
Prompt to analyze a spreadsheet in ChatGPT without changing data blindly: diagnosis, validation, and a reproducible report
A reusable prompt for analyzing Excel or CSV files with schema inventory, data-quality diagnosis, a plan before edits, calculation traceability, and sanity checks.
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How to test prompt injection in AI agents before connecting email, PDF, RAG, and tools
A reproducible method to prove whether untrusted content can divert an agent, trigger tools, leak data, or persist instructions before production.
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Temperature, top_p, and top_k in 2026: what still works in OpenAI, Claude, and Gemini
In 2026, classic sampling controls are no longer universal. See where they still work, where they cause errors, and which settings control variability now.
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Project Memory in ChatGPT: default vs. project-only and what enters context in 2026
Understand how ChatGPT Project memory modes affect instructions, files, chats, and outside context, including plan differences and tests for isolation.
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System prompt, developer message, and user prompt: how instruction hierarchy changes across OpenAI, Claude, and Gemini
OpenAI, Claude, and Gemini separate high-level instructions from the user's request, but their APIs use different roles, fields, and precedence rules.
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Prompt to study with ChatGPT using PDF: active recall, correction and answers based on the material
Use a modular prompt to turn PDFs, slides, and notes into an active recall session, one question at a time, proofreading material, and reviewing weaknesses.
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Structured Outputs or “respond in JSON”? When to use JSON Schema in OpenAI, Claude and Gemini
Requesting JSON, enabling an output mode and imposing a schema solve different problems. See when each approach is sufficient and where it is still necessary to validate content.
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Mega-prompt or prompt chaining? When to split a task into steps and when to keep it to a single call
A practical guide to deciding between a single call and a staged flow, comparing dependencies, validation, failures, state, latency, cost, and testing.
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Does your agent call the wrong tool? How names, descriptions and schemas improve tool calling
Tool calling errors are rarely resolved with more prompting. See how to reduce ambiguity with specific names, usage descriptions, restricted schemas, enums, and confusion tests.
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- 01MCP vs. function calling: what each layer does, when to use it and why they can work togetherAgents and Automation
- 02Structured Outputs or “respond in JSON”? When to use JSON Schema in OpenAI, Claude and GeminiPrompt Engineering
- 03Temperature, top_p, and top_k in 2026: what still works in OpenAI, Claude, and GeminiAI Performance
- 04Does your agent call the wrong tool? How names, descriptions and schemas improve tool callingAgents and Automation
- 05Project Memory in ChatGPT: default vs. project-only and what enters context in 2026Context, Tokens and Memory
Ready-to-Use Prompts
Prompts to copy, adapt and use in real tasks, accompanied by instructions, variables, examples and limitations.
Prompt to compare two spreadsheet versions and explain every difference without changing the data
A prompt template for comparing File A and File B in read-only mode, finding differences in structure, values, formulas, and formatting, and producing a traceable report.
Read articleCopilot in Excel with Python: 9 prompts to clean data, test hypotheses, and build auditable analyses
Ready-to-use prompts for Python through Copilot in Excel with explicit inputs, checkpoints, validation, and an audit trail.
Read articlePrompt to analyze a spreadsheet in ChatGPT without changing data blindly: diagnosis, validation, and a reproducible report
A reusable prompt for analyzing Excel or CSV files with schema inventory, data-quality diagnosis, a plan before edits, calculation traceability, and sanity checks.
Read articlePrompt to study with ChatGPT using PDF: active recall, correction and answers based on the material
Use a modular prompt to turn PDFs, slides, and notes into an active recall session, one question at a time, proofreading material, and reviewing weaknesses.
Read articlePrompt Engineering
Techniques for creating, structuring, testing, and refining prompts for different AI tasks and models.
Prompts with examples: when few-shot helps and when it only adds noise
Learn when examples genuinely improve a prompt, when they only add unnecessary context, and how to compare zero-shot and few-shot with a representative test set.
Read articleZero-shot vs. few-shot: when examples improve a prompt and when they waste context
Compare instructions without examples and prompts with demonstrations on the same test cases to measure format, accuracy, and token cost.
Read articleSystem prompt, developer message, and user prompt: how instruction hierarchy changes across OpenAI, Claude, and Gemini
OpenAI, Claude, and Gemini separate high-level instructions from the user's request, but their APIs use different roles, fields, and precedence rules.
Read articleStructured Outputs or “respond in JSON”? When to use JSON Schema in OpenAI, Claude and Gemini
Requesting JSON, enabling an output mode and imposing a schema solve different problems. See when each approach is sufficient and where it is still necessary to validate content.
Read articleContext, Tokens and Memory
Context windows, tokens, memory, files, long conversations and ways to provide information to AI.
Prompt caching in practice: how to separate static and variable context without breaking the cache
Prompt caching reduces cost and latency when calls reuse the same prefix. See how to separate static and variable content and measure cache hits without relying on guesswork.
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RAG or long context: when to send all documents, when to use retrieval, and when to combine the two
A practical matrix to choose between long context, RAG and hybrid architecture considering corpus, update, question type, citations, cost, latency and maintenance.
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Why does AI forget instructions in long conversations? Context window, context rot and clutter-free memory
When an AI starts to ignore old rules, the problem may be context limits, degradation within the window itself, conflicting instructions or misunderstood memory.
Read articleProject Memory in ChatGPT: default vs. project-only and what enters context in 2026
Understand how ChatGPT Project memory modes affect instructions, files, chats, and outside context, including plan differences and tests for isolation.
Read articleAI Performance
Tests, comparisons and methods to improve the quality, accuracy, consistency and efficiency of responses.
Did your prompt get worse after switching models? How to diagnose regression before rewriting everything
If a workflow changed after a model change, preserve the prompt as a baseline, isolate variables and test format, tools, context and parameters before rewriting.
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How to know if a prompt really improved: test cases, rubric and regression without evaluating on feeling
A practical method for comparing prompt versions with representative cases, rubric defined before responses, blind A/B testing, and recording regressions.
Read articleTemperature, top_p, and top_k in 2026: what still works in OpenAI, Claude, and Gemini
In 2026, classic sampling controls are no longer universal. See where they still work, where they cause errors, and which settings control variability now.
Read articleFrom ready-made prompts to advanced AI use
From ready-made prompts to advanced AI use.