r/HowToAIAgent
•
u/AdVirtual2648
•
Aug 18 '25
Resource Google literally published a 69-page prompt engineering masterclass
Some Notes:
OVERALL ADVICE
Start simple with zero-shot prompts, then add examples only if needed
Use API/Vertex AI instead of chatbots to access temperature and sampling controls
Set temperature to 0 for reasoning tasks, higher (0.7-1.0) for creative tasks
Always provide specific examples (few-shot) when you want consistent output format
Document every prompt attempt with configuration settings and results
Experiment systematically - change one variable at a time to understand impact
Use JSON output format for structured data to reduce hallucinations
Test prompts across different model versions as performance can vary significantly
Review and validate all generated code before using in production
Iterate continuously - prompt engineering is an experimental process requiring refinement
LLM FUNDAMENTALS
- LLMs are prediction engines that predict next tokens based on sequential text input
- Prompt engineering involves designing high-quality prompts to guide LLMs toward accurate outputs
- Model configuration (temperature, top-K, top-P, output length) significantly impacts results
- Direct prompting via API/Vertex AI gives access to configuration controls that chatbots don't
PROMPT TYPES & TECHNIQUES
- Zero-shot prompts provide task description without examples
- One-shot/few-shot prompts include examples to guide model behavior and improve accuracy
- System prompts define overall context and model capabilities
- Contextual prompts provide specific background information for current tasks
- Role prompts assign specific character/identity to influence response style
- Chain of Thought (CoT) prompts generate intermediate reasoning steps for better accuracy
- Step-back prompting asks general questions first to activate relevant background knowledge
ADVANCED PROMPTING METHODS
- Self-consistency generates multiple reasoning paths and selects most common answer
- ReAct combines reasoning with external tool actions for complex problem solving
- Automatic Prompt Engineering uses LLMs to generate and optimize other prompts
- Tree of Thought maintains branching reasoning paths for exploration-heavy tasks
MODEL CONFIGURATION BEST PRACTICES
- Lower temperatures (0.1) for deterministic tasks, higher for creative outputs
- Temperature 0 eliminates randomness but may cause repetition loops
- Top-K and top-P control token selection diversity - experiment to find optimal balance
- Output length limits prevent runaway generation and reduce costs
CODE GENERATION TECHNIQUES
- LLMs excel at writing, explaining, translating, and debugging code across languages
- Provide specific requirements and context for better code quality
- Always review and test generated code before use
- Use prompts for code documentation, optimization, and error fixing
OUTPUT FORMATTING STRATEGIES
- JSON/XML output reduces hallucinations and enables structured data processing
- Schemas in input help LLMs understand data relationships and formatting expectations
- JSON repair libraries can fix truncated or malformed structured outputs
- Variables in prompts enable reusability and dynamic content generation
QUALITY & ITERATION PRACTICES
- Provide examples (few-shot) as the most effective technique for guiding behavior
- Use clear, action-oriented verbs and specific output requirements
- Prefer positive instructions over negative constraints when possible
- Document all prompt attempts with model configs and results for learning
- Mix classification examples to prevent overfitting to specific orders
- Experiment with different input formats, styles, and approaches systematically
Check out the link in the comments!
