# Advanced Prompt Engineering Techniques at Instacart Instacart, a leading e-commerce platform, has been rapidly adopting LLMs and GenAI technologies for various internal and customer-facing applications. This case study explores their practical experience implementing and optimizing prompt engineering techniques for production LLM applications. ## Company Background and Use Cases - Developed internal assistant called Ava - Created AI-powered search feature "Ask Instacart" - Focused on delivering value across multiple stakeholders: ## Technical Implementation Details ### Model Selection and Considerations - Primary focus on GPT-4 implementation - Some techniques also tested with GPT-3.5 - Recommendation to use GPT-4 when economically feasible due to superior performance - Techniques developed through combination of: ### Traditional Prompt Engineering Techniques ### Chain of Thought (CoT) - Implementation using simple phrases like "Let's take this step by step" - Alternative approach using "Take a deep breath and come up with a plan" - Particularly effective for complex tasks requiring structured thinking ### ReAct Pattern - Implemented action-based prompting system - Enabled external actions through specific commands: - Similar to ChatGPT's plugin system but customized for internal use ### Novel Prompt Engineering Techniques ### Room for Thought - Explicitly encourages LLM to create plans before answering - Implementation requires careful prompt construction to prevent premature answers - Example use case: Pull request title and description generation - Can include pre-baked guidelines to save generation tokens ### Monte Carlo Technique - Generates multiple different options before final answer - Particularly effective for creative tasks - Implementation details: ### Self Correction - Enables model to critique its own outputs - Often combined with Monte Carlo technique - Process: ### Classifying Technique - Uses logit_bias parameter to force specific token selection - Implementation details: - Supports "deep thought" mode for combining with other techniques ### Puppetry Technique - Manipulates conversation state to guide model behavior - Implementation approach: ## Production Considerations ### Performance Optimization - Careful consideration of token usage - Pre-baking static guidelines into prompts - Using multiple rounds of prompting when needed - Temperature adjustment for different use cases ### Output Reliability - Implementation of forced choice responses - Use of logit_bias for controlled outputs - Multiple validation steps in complex tasks - Combination of techniques for optimal results ### Integration Points - OpenAI API integration - Custom internal LLM proxy - Conversation state management - Format validation and processing ## Results and Impact ### Successful Applications - Internal productivity tooling - Code review automation - Search functionality - Employee assistance ### Best Practices Developed - Technique selection based on use case - Prompt optimization for token efficiency - Output validation strategies - Interactive refinement processes ## Technical Challenges and Solutions ### Common Issues Addressed - Context size limitations - Hallucination prevention - Task completion reliability - Output format consistency ### Implementation Solutions - Multi-step prompting processes - Forced output formatting - Validation through multiple techniques - Temperature and bias parameter optimization ## Future Considerations ### Scalability - Token usage optimization - Processing efficiency - Response time management - Cost considerations ### Maintenance - Prompt template management - Technique documentation - Performance monitoring - Continuous improvement processes