Prompt Engineering for JSON: Extract Structured Output
JSON extraction is less about asking the LLM to "give me JSON" and more about teaching it to parse and format like a JSON parser.
Prompt Engineering Articles
50 articles
Prompt Engineering Latency: Reduce Time-to-First-Token
The fastest way to get a response from a large language model isn't by asking it to be faster, but by making it ask itself a question it already knows t.
Llama Prompt Engineering: Open-Source Model Techniques
Prompt engineering for open-source LLMs like Llama is less about "telling" the model what to do and more about "guiding" its latent capabilities through.
Meta-Prompting: Generate Better Prompts with LLMs
Meta-prompting, the art of using an LLM to generate or refine prompts for other LLM interactions, is surprisingly effective because LLMs are surprisingl.
Prompt Engineering Multi-Turn: Manage Conversation State
The most surprising thing about managing multi-turn conversations with LLMs is that the model doesn't "remember" anything between turns; you have to exp.
Prompt Engineering for Multilingual: Cross-Language Prompts
The most surprising thing about cross-language prompts is that the model often performs better when you ask it to translate and then answer, rather than.
Negative Prompting: What to Tell LLMs Not to Do
Negative Prompting: What to Tell LLMs Not to Do — practical guide covering prompt-engineering setup, configuration, and troubleshooting with real-world ...
Prompt Management in Production: Version and Deploy Prompts
Prompt Management in Production: Version and Deploy Prompts — practical guide covering prompt-engineering setup, configuration, and troubleshooting with...
Prompt Chaining: Build Multi-Step LLM Pipelines
Prompt chaining lets you break down complex tasks into a series of smaller, manageable LLM calls, each building on the output of the previous one.
Prompt Versioning: Track and Roll Back Prompt Changes
Prompt versioning is crucial because the "best" prompt for a given task is rarely static, and tracking changes is essential for reproducibility and debu.
Promptfoo: Test and Evaluate Prompts Automatically
Promptfoo: Test and Evaluate Prompts Automatically — practical guide covering prompt-engineering setup, configuration, and troubleshooting with real-wor...
Prompt Engineering Q&A: Improve Answer Accuracy
Prompt engineering is less about crafting the perfect sentence and more about understanding the underlying statistical patterns a language model has lea.
Prompt Engineering for RAG: Optimize Retrieval Queries
Retrieval augmented generation RAG systems can tell you things they weren't explicitly trained on, but only if you ask the right questions.
ReAct Prompting: Reasoning and Acting with LLMs
The most surprising thing about ReAct prompting is that it doesn't just make LLMs smarter, it makes them more honest about what they don't know.
Role Prompting: Assign Personas for Better Outputs
Role prompting lets you assign a persona to the AI, guiding its response style and knowledge domain. Let's see this in action
Self-Consistency Prompting: Majority Vote for Accuracy
Self-consistency prompting gets better results by having the LLM generate multiple different reasoning paths to the same answer, then picking the most f.
Prompt Engineering for SQL: Text-to-SQL Generation
Text-to-SQL generation models can sometimes produce syntactically incorrect SQL queries or queries that don't align with the user's intent, often due to.
Prompt Decomposition: Break Complex Tasks into Steps
You've probably noticed that asking an LLM to do something super complicated in one go often results in a confused mess.
Prompt Engineering Structured Output: Every Pattern
The most surprising truth about structured output in LLMs is that it's not about telling the model what to do, but showing it.
Prompt Engineering Summarization: Condense Any Document
The most surprising thing about prompt engineering for summarization is that the length of your prompt is often inversely proportional to its effectiven.
System Prompt Best Practices: Consistent LLM Behavior
LLM "hallucinations" aren't actually hallucinations; they're the emergent behavior of a probabilistic model trained to predict the next most likely toke.
Prompt Engineering Temperature: Control Output Creativity
The temperature parameter in LLM prompts doesn't just make output "more creative"; it fundamentally reshapes the probability distribution of the next to.
Prompt Token Budgets: Reduce Cost Without Losing Quality
A prompt's token budget isn't just about how much text you can send; it's about how much the model actually sees and processes.
Tree-of-Thought Prompting: Explore Multiple Reasoning Paths
The most surprising thing about Tree-of-Thought ToT prompting is that it doesn't actually make the LLM "think" more, but rather it forces it to show its.
Prompt Formatting: XML vs Markdown for Clarity
XML and Markdown are fundamentally different approaches to structuring text, and their suitability for prompt formatting hinges on whether you prioritiz.
Zero-Shot Prompting: Get Results Without Examples
Zero-shot prompting unlocks LLM capabilities by asking for tasks it hasn't been explicitly trained on, relying solely on its vast pre-training knowledge.
Prompt Engineering A/B Testing: Compare Prompt Performance
The most surprising thing about A/B testing prompts is that the "better" prompt often isn't the one that's more human-sounding, but the one that more pr.
Prompt Engineering Robustness: Defend Against Adversarial Inputs
Adversarial inputs don't just trick LLMs into saying bad things; they exploit the fundamental way LLMs process information, revealing a surprising fragi.
Prompt Engineering for Agents: Tool Use and Action Prompts
The most surprising thing about prompt engineering for agents is that the "prompt" isn't just a static string of text; it's a dynamic, multi-turn conver.
Prompt Engineering Batch Processing: Scale LLM Pipelines
Prompt engineering in batch processing isn't about finding the "best" prompt; it's about designing prompts that are robust enough to handle variations i.
Chain-of-Thought Prompting: Make LLMs Show Their Work
Chain-of-Thought CoT prompting is the secret sauce that makes Large Language Models LLMs surprisingly good at tasks requiring multi-step reasoning, not .
Prompt Engineering Citations: Ground Answers in Sources
We often think of prompt engineering as crafting creative instructions to elicit novel outputs from LLMs, but its real power lies in making LLMs reliabl.
Prompt Engineering Classification: Route Queries to Agents
This system lets you dynamically route incoming user queries to the most appropriate AI agent based on the query's content.
Claude Prompt Engineering: Anthropic-Specific Techniques
Claude Prompt Engineering: Anthropic-Specific Techniques — practical guide covering prompt-engineering setup, configuration, and troubleshooting with re...
Prompt Engineering for Code: Generate and Debug Code
You can get an LLM to write code for you, sure, but the real magic is getting it to debug code, and it's way more powerful when you combine the two.
Prompt Engineering Context: Fit More Into Every Window
You can fit dramatically more context into your LLM prompts than you probably think, and the trick isn't just making your prompt shorter.
Prompt Engineering Cost: Reduce Token Usage by 50%
You can slash your LLM token costs by half, not by choosing a cheaper model, but by making your existing prompts dramatically more efficient.
Prompt Delimiters: Separate Context and Instructions
Prompt delimiters are the secret sauce that lets you tell large language models where the "stuff to think about" ends and "what to do with it" begins.
Prompt Engineering Domain Adaptation: Fine-Tune Prompts
Adapting prompts is like teaching a highly skilled but literal-minded assistant a new jargon – you're not retraining the assistant, just refining their .
DSPy Prompt Optimization: Automate Prompt Improvement
DSPy's magic is that it treats prompts not as static strings, but as compiled programs that can be automatically optimized.
Prompt Engineering Governance: Manage Prompts at Scale
Prompt engineering governance is less about controlling users and more about building a shared understanding of what works and why.
Prompt Engineering Entity Extraction: Parse Structured Data
The most surprising truth about prompt engineering for entity extraction is that it's often less about crafting the perfect "prompt" and more about care.
Prompt Evaluation: Automated LLM Quality Assessment
Prompt evaluation is surprisingly more about evaluating the prompt than evaluating the LLM. Let's see how this plays out
Few-Shot Prompting: Choose the Best Examples
Choosing the right examples for few-shot prompting is more about understanding the underlying mechanics of the LLM than about picking the "best" ones in.
Prompt Engineering for Function Calling: Tool Use Patterns
The most surprising thing about prompt engineering for function calling is that the LLM doesn't actually understand the functions you give it in the way.
Prompt Engineering Fundamentals: Start Here
The most surprising thing about prompt engineering is that you're not actually "engineering" anything; you're negotiating.
Gemini Prompt Engineering: Google-Specific Techniques
Gemini's prompt engineering is less about crafting the perfect sentence and more about understanding that you're not just talking to a chatbot, but a so.
GPT-4o Prompt Optimization: Get the Best Results
GPT-4o's ability to process multimodal inputs and deliver faster, more coherent responses means prompt engineering is more critical than ever, but also .
Prompt Engineering Anti-Hallucination: Reduce LLM Errors
Large Language Models don't "hallucinate" in the human sense; they're statistical machines that generate sequences of words based on probability, and so.
Prompt Instructions: Write Clear and Unambiguous Prompts
Crafting clear and unambiguous prompts is less about telling the AI what to do, and more about architecting the context within which it makes its decisi.