# Designing Human-in-the-Loop Experiences for LLMs: Production Challenges and Insights ## Company Background and Context V7 is a training data platform company that manages ground truth data for hundreds of AI companies. Their unique position in handling vast amounts of well-labeled training data (petabytes) provides them with valuable insights into what constitutes good knowledge for neural networks and LLMs. ## Current State of LLMs in Production ### Limited Implementation Reality - Most production implementations of LLMs remain relatively simplistic - Companies often rely on basic feedback mechanisms (thumbs up/down) - LLMs are frequently used as "glorified zero-shot models" - In computer vision applications, LLMs are mainly used to manipulate other models rather than direct implementation ### The Co-Pilot Misconception - Industry often thinks of LLMs as co-pilots, but implementation differs from this ideal - Unlike true co-pilot systems (like in aviation), LLM interactions are typically discrete and task-based - Current implementations lack continuous awareness and support of ongoing tasks - Most interactions are simple query-response patterns rather than continuous assistance ## Major Production Challenges ### Automation Limitations - Automation is often overrated in real-world applications - End-to-end automation systems take significant time to implement successfully - LLMs, despite impressive language capabilities, often don't perform significantly better than smaller, fine-tuned models - Industry use cases often have strictly defined discrete outcomes, limiting the utility of LLMs' broad reasoning capabilities ### Human-in-the-Loop Challenges - Difficulty in designing effective learning mechanisms from human feedback - People often provide incorrect information or poorly formatted training data - Information asymmetry issues where correct answers may be wrong for specific contexts - Challenge of automating expert-level tasks while maintaining accuracy ### Technical Implementation Issues - Many industry use cases have no room for error - Lack of "undo" functionality in critical applications - Integration challenges with existing specialized software systems - Balance between automation and human oversight ## V7's Product Implementation ### Auto Label Feature - Implements multimodal co-pilot functionality - Can understand both language instructions and visual content - Allows for complex tasks like airplane segmentation based on specific criteria - Demonstrates the challenges of automating expert-level tasks ### Integration Challenges - Difficulty in automating tasks for expert users - Handling outer distribution cases - Balancing automation with quality assurance - Managing the transition from over-supervised data to larger, less precisely labeled datasets ## Industry-wide Implementation Patterns ### Software Integration Approaches - Tension between traditional SaaS interfaces and LLM implementations - Examples from companies like Adept for website navigation - OpenAI's implementation of simple interfaces - Notable implementations by companies like Sana and Glean for visual feedback ### Future Considerations - Need for more sophisticated feedback mechanisms beyond simple binary responses - Challenge of implementing continuous learning systems - Balance between model capabilities and practical industry requirements - Integration with existing professional tools and workflows ## Best Practices and Recommendations ### System Design - Consider the specific industry requirements rather than implementing generic LLM solutions - Focus on well-defined outcomes for critical applications - Design appropriate feedback mechanisms for continuous improvement - Balance automation with human expertise ### Implementation Strategy - Start with simple, reliable implementations rather than complex automation - Consider the specific needs of expert users - Implement appropriate quality control measures - Design for scalability and ongoing improvement ### Common Pitfalls to Avoid - Over-automation of critical processes - Relying too heavily on non-expert human feedback - Ignoring industry-specific requirements and constraints - Implementing overly complex solutions for simple problems