LLM Usage
Prompt Engineering β Getting the Most Out of Language Models
Prompt engineering is the art and science of designing effective inputs to LLMs. This guide covers prompting techniques, sampling strategies, and systematic best practices for reliable, high-quality outputs.
- Zero-Shot to Few-Shot β Progressive techniques for task framing
- Chain-of-Thought β Elicit step-by-step reasoning for complex problems
- Systematic Testing β Treat prompts like code with version control
The quality of the output is determined by the quality of the input.
Prompt Engineering
Prompt engineering is the art and science of designing effective inputs to LLMs. This tutorial covers prompting techniques, sampling strategies, and systematic best practices.
DfPrompt Engineering
Prompt engineering is the process of designing and optimizing input prompts to elicit desired behaviors from language models. It encompasses techniques for framing tasks, providing context, and controlling output characteristics without modifying model parameters.
Prompting Techniques
Zero-Shot Prompting
The model performs the task based solely on the instruction, with no examples provided. This relies on the model's pre-trained knowledge and generalization ability.
Few-Shot Prompting
Provide demonstrations of the desired input-output mapping. The model learns the pattern from examples and applies it to new inputs. Typically 2-8 examples are sufficient.
Chain-of-Thought Prompting
Instruct the model to show its reasoning process step by step. This dramatically improves performance on arithmetic, logic, and multi-step problems.
Tree-of-Thought Prompting
Explore multiple reasoning branches, evaluate each, and select the best one. Useful for complex planning and decision-making tasks.
System Prompts
System prompts set the model's behavior, role, and constraints. They are prepended to the conversation and guide all subsequent interactions.
Temperature and Sampling
Temperature Scaling
Temperature Scaling
Here,
- =Logit for token t
- =Temperature parameter
- =Vocabulary index
- T = 0: Greedy decoding (deterministic)
- T = 0.7: Moderate creativity (recommended)
- T = 1.0: Sample from model distribution
- T > 1.0: High randomness (creative tasks)
Top-k Sampling
Top-k Sampling
Here,
- =Top-k most probable tokens
- =Number of candidates
Top-p (Nucleus) Sampling
Here,
- =Nucleus set of tokens
- =Cumulative probability threshold
Top-p dynamically adjusts the candidate set size based on the probability distribution.
Sampling Parameter Guide
| Task | Temperature | Top-p | Top-k |
|---|---|---|---|
| Code generation | 0.0-0.2 | 0.9 | - |
| Factual QA | 0.0-0.3 | 0.9 | - |
| Creative writing | 0.7-1.0 | 0.9-0.95 | 50 |
| Brainstorming | 0.8-1.2 | 0.95 | 100 |
| Translation | 0.0-0.3 | 0.9 | - |
Sampling Implementation
`python from transformers import AutoModelForCausalLM, AutoTokenizer import torch
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf") tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf") inputs = tokenizer("The future of AI is", return_tensors="pt")
output = model.generate( **inputs, max_new_tokens=100, temperature=0.7, top_p=0.9, do_sample=True, ) print(tokenizer.decode(output[0], skip_special_tokens=True)) `
Structured Output Prompting
Guide the model to produce structured outputs like JSON, tables, or specific formats. Use explicit format instructions and delimiters to ensure consistent output.
Best Practices
Do:
- Be specific and clear about the task
- Provide relevant context and constraints
- Use delimiters to separate input from instructions
- Specify the desired output format
- Test with diverse inputs
Do Not:
- Assume the model knows your implicit context
- Use ambiguous instructions
- Overload prompts with too many tasks
- Ignore the model output length limits
- Use sarcastic or ironic instructions
The most effective prompts follow a clear structure: Role, Context, Task, Format, Constraints (RCTFC). This ensures the model has all necessary information to generate the desired output.
Practice Exercises
- Design a prompt that extracts structured data from unstructured text. Test with 5 different inputs.
- Compare zero-shot vs few-shot performance on a classification task. How many examples are needed for peak performance?
- Experiment with temperature settings from 0.0 to 1.5. At what point does output quality degrade?
- Design a system prompt for a code review assistant. Test it on 3 different code snippets.
Key Takeaways:
- Prompt engineering optimizes inputs without modifying model parameters
- Zero-shot, few-shot, and chain-of-thought are fundamental techniques
- Temperature, top-k, and top-p control output randomness
- Be specific, provide context, and specify output format
- System prompts set model behavior and constraints
- Structured output prompting improves downstream usability
Advanced Prompting Techniques
Prompt Chaining
Break complex tasks into smaller prompts executed sequentially. The output of one prompt becomes the input to the next. This improves reliability for multi-step tasks.
Prompt Caching
For repeated queries, cache the system prompt computation to reduce latency and cost. Many LLM APIs now support prompt caching natively.
Meta-Prompting
Use the LLM to generate or optimize prompts. Ask the model to improve your prompt based on desired behavior. This creates a feedback loop for prompt optimization.
Constrained Generation
Use techniques like JSON mode, grammar-constrained decoding, or regex filtering to ensure outputs conform to specific formats. Libraries like Outlines and guidance make this easy.
Prompt Optimization
DSPy Framework
DSPy provides a programmatic approach to prompt optimization. Instead of manually crafting prompts, you define the task signature and let the framework optimize the prompt automatically using teleprompters.
Automatic Prompt Engineering
Use LLMs to generate and evaluate multiple prompt variants. Select the best-performing prompt based on a validation set. This is more systematic than manual prompt engineering.
Prompt Security
Injection Attacks
Prompt injection occurs when user input manipulates the system prompt to override intended behavior. Always validate and sanitize user inputs. Use delimiter-based defenses and instruction hierarchy.
Jailbreak Prevention
Jailbreaking attempts to bypass safety guardrails. Defense strategies include: system prompt hardening, output filtering, content classifiers, and red-team testing.
Prompt engineering is an evolving field. New techniques emerge regularly. Stay current with research papers and community best practices for the latest developments.
Prompt Testing and Debugging
Systematic prompt testing is essential for production deployments. Create a test suite with diverse inputs, edge cases, and adversarial examples. Track metrics like accuracy, latency, and cost across prompt versions.
Prompt Version Control
Treat prompts like code. Use version control, review processes, and A/B testing. Document the rationale behind each prompt change and track performance metrics over time.
Common Prompt Pitfalls
- Overly specific prompts that do not generalize to new inputs.
- Implicit assumptions that the model cannot satisfy.
- Contradictory instructions that confuse the model.
- Missing edge case handling for unexpected inputs.
- Ignoring token limits and truncation behavior.
What to Learn Next
-> In-Context Learning Teaching LLMs new tasks without trainingβpurely through prompts.
-> Chain-of-Thought Reasoning Making LLMs think step by step for complex reasoning problems.
-> RAG System Design Building production-ready retrieval systems for grounded generation.
-> Retrieval-Augmented Generation Combining LLMs with external knowledge for accurate, cited answers.
-> LLM Agent Frameworks Building autonomous agents that reason, plan, and act.
-> Building Production LLM Apps From prototype to production: deploying LLMs at scale.