This case study, presented by a Weights & Biases founder, demonstrates the practical challenges and solutions in bringing LLM applications from demo to production through a personal project building an open-source voice assistant. The presentation provides valuable insights into the broader landscape of LLMOps while using a specific implementation example to illustrate key concepts. The speaker begins by highlighting a crucial observation in the current AI landscape: while AI demos are remarkably easy to create, productionizing AI applications presents significant challenges. This gap between demo and production has led many organizations to release suboptimal AI products, highlighting the need for proper LLMOps practices. The case study revolves around building a voice assistant similar to Alexa, inspired by the speaker's daughter's interactions with smart speakers. The project aimed to create a system that could understand voice commands and execute various "skills" like playing music, checking weather, and performing calculations. This serves as an excellent example of how modern LLMs can be used to build practical applications that previously required specialized proprietary systems. The technical architecture implemented includes: * Speech recognition using Whisper for audio transcription * Local LLM deployment using llama.cpp * A skills framework for executing various commands * Natural language understanding to convert speech into function calls The development process revealed several key LLMOps learnings: **Model Selection and Iteration:** * Started with Llama 2 7B model running on affordable hardware (Rock Pi) * Tested multiple models including Mistral * Demonstrated the importance of being able to switch models easily as new options become available * Showed how different models can provide incremental improvements (Mistral provided a 4% accuracy boost over Llama 2) **Performance Optimization:** * Latency proved crucial for user experience * Required careful model size selection to maintain real-time performance * Demonstrated the balance between model capability and hardware constraints **Systematic Improvement Process:** * Initial implementation had 0% accuracy * Basic prompt engineering improved results but still insufficient * Model switching (to Llama Chat) brought accuracy to 11% * Structured error analysis and feedback incorporation raised accuracy to 75% * Switch to Mistral improved to 79% * Fine-tuning with LoRA achieved final 98% accuracy **Data and Training:** * Created training data through manual examples * Used larger models (ChatGPT) to generate additional training data * Implemented careful data filtering and validation * Demonstrated practical use of fine-tuning for specific use cases The case study emphasizes several critical aspects of successful LLMOps implementation: **Evaluation Framework:** * Highlights the importance of moving beyond "vibes-based" testing * Recommends multiple evaluation sets: * Critical test cases (100% accuracy required) * Quick tests for rapid iteration * Comprehensive nightly evaluation suites * Stresses the importance of metrics that correlate with actual user experience **Development Approach:** * Start with lightweight prototypes * Incorporate end-user feedback early and often * Use iterative improvement processes * Maintain comprehensive tracking of experiments, including failures The speaker emphasizes that tracking everything is crucial for reproducibility and knowledge retention. This includes: * All experimental attempts, including failures * Parameter configurations * Training data and variations * Performance metrics and evaluations The case study also reveals interesting insights about modern LLM development: * The accessibility of powerful open-source models * The feasibility of running substantial LLM applications on consumer hardware * The importance of systematic evaluation in driving improvements * The complementary nature of different optimization techniques (prompt engineering, model selection, fine-tuning) A particularly valuable insight is how the project naturally evolved to use multiple techniques rather than relying on a single approach. This mirrors enterprise experiences where successful deployments typically combine various methods to achieve desired performance levels. The speaker concludes by emphasizing that proper evaluation frameworks are the foundation of successful LLM applications, enabling informed decisions about which techniques to apply and when. This systematic approach to evaluation and improvement stands in stark contrast to the common pattern of rushing AI applications to production based solely on impressive demos. In terms of tooling, the project showcases the use of Weights & Biases for experiment tracking and evaluation, though the speaker emphasizes that the principles apply regardless of specific tools used. The case study effectively demonstrates how proper LLMOps practices can transform an interesting demo into a production-ready system, while highlighting the importance of systematic evaluation, careful iteration, and comprehensive tracking of the development process.