Prompt Design Patterns
I design and optimize prompts using proven techniques that make LLM outputs predictable, accurate, and useful. My approach combines academic research with hands-on production experience across OpenAI, Anthropic, and open-source models.
Few-Shot & Chain-of-Thought Prompting
I structure prompts with carefully selected examples (few-shot) and step-by-step reasoning chains (chain-of-thought) to improve accuracy on complex tasks. This is particularly effective for mathematical reasoning, data extraction, and multi-step analysis.
ReAct & Agentic Patterns
For autonomous AI agents, I implement ReAct (Reasoning + Acting) patterns that let LLMs reason about tasks, take actions, and observe results in a loop. This powers agents that can search databases, call APIs, and make decisions based on real-time information.
Self-Consistency & Ensembling
For high-stakes outputs, I deploy self-consistency strategies that run multiple reasoning paths and select the most consistent answer. This dramatically reduces hallucination rates and improves factual accuracy.
Evaluation & Quality Metrics
Good prompts require rigorous testing. I build evaluation harnesses that measure prompt quality across dimensions: accuracy, relevance, tone consistency, safety, and latency. These evals run as part of CI/CD pipelines, catching regressions before they reach production. Key metrics include exact match, semantic similarity, refusal rates, and token efficiency.
Safety, Guardrails & Cost Optimization
Production AI systems need safeguards. I implement output validation layers, refusal handling for out-of-scope inputs, and adversarial testing to harden prompts against injection attacks. On the cost side, I optimize prompt lengths through compression techniques, implement model routing (cheaper models for simple tasks), and design caching strategies that reduce API calls without sacrificing quality.
Frequently Asked Questions
What is prompt engineering?
Prompt engineering is the systematic design and optimization of inputs to large language models to produce reliable, accurate, and useful outputs. It combines aspects of software engineering, linguistics, and machine learning.
What tools and frameworks do you use?
I work with LangChain, LlamaIndex, OpenAI APIs, Anthropic Claude, vector databases (Pinecone, Weaviate), and evaluation frameworks. I also build custom tooling for prompt management and testing.
Can you integrate AI into an existing application?
Yes. I specialize in adding AI features to existing products — chatbots, content generation, data extraction, and intelligent search. Integration typically involves API design, context management, and careful prompt engineering.
How do you measure prompt quality?
I use automated evaluation metrics (exact match, semantic similarity, BLEU/ROUGE), human evaluation rubrics, A/B testing in production, and continuous monitoring of user feedback and error rates.
Do you work with open-source LLMs?
Yes. While I frequently use OpenAI and Anthropic, I also work with open-source models like Llama, Mistral, and Gemma for on-premise deployments where data privacy or cost is a concern.