# Multiple LLM Production Case Studies: Lessons from the Field ## Overview This case study presents multiple real-world implementations of LLM-based systems across different industries, highlighting common challenges, solutions, and key lessons learned in deploying LLMs in production environments. ## Case Study 1: Consulting Firm's Financial Database Search ### Background - Consulting firm needed to implement semantic search functionality for a large financial database - Database contained market caps, valuations, mergers, acquisitions, and other financial data - Data was highly proprietary and valuable, preventing creation of supplementary databases - Main challenge centered around text-heavy SQL database fields ### Technical Challenges - Similarity definition problems in financial context - Extremely low tolerance for spurious retrieval - Requirements for quick response times - Need for consistent results across different queries - Unable to modify existing database structure ### Solution Implementation - Developed text-to-SQL approach for structured data queries - Used LLM to generate relevant keywords for search context - Implemented post-retrieval filtering and reranking - Lean processing to maintain performance requirements ### Key Lessons - Most retrieval-augmented generation requires data source modification - Business definitions of similarity differ from technical implementations - Graph databases might be better suited than vector databases for certain use cases - Important to establish clear validation points early in the project ## Case Study 2: Automotive Showroom Virtual Assistant ### Background - Car manufacturer wanted to create an interactive showroom experience - Project involved partnership with major cloud provider - Required handling of non-canonical documentation across different countries - Needed to maintain brand identity and handle restricted information ### Technical Challenges - Working with legacy LLM models - Cloud service-induced hallucinations in file search - Restrictions on discussing certain topics (pricing, competitors) - Complex comparison requirements against unrestricted chatbots ### Solution Implementation - Implemented multi-layer LLM processing - Created custom document summarization pipeline - Developed strict hallucination prevention mechanisms - Built custom metadata generation system ### Key Lessons - Critical importance of measuring at every processing stage - Need to break down RAG systems into measurable components - Importance of canonical data sources - Value of clear documentation and source truth ## Case Study 3: Information and Sales Assistant ### Background - Project aimed to leverage success cases and industry reports - Built in early 2023 with less mature frameworks - Needed to handle both technical reports and success cases - Required measurable effectiveness metrics ### Technical Challenges - Different document formats requiring different processing approaches - Complex source selection logic - Difficulty in measuring effectiveness - Limited access to source format information ### Solution Implementation - Combined vector search with structured SQL database approach - Implemented agentic workflows for quality improvement - Created custom pre-structuring for document processing ### Key Lessons - Not all solutions require vector databases - Engineering work often more challenging than LLM integration - Importance of proper data access and processing pipelines ## Case Study 4: Banking Code Copilot ### Background - International bank wanted custom GitHub Copilot alternative - Initially restricted to GPT-3.5 due to budget constraints - Lack of clear success metrics - Limited understanding of fine-tuning requirements ### Key Lessons - Importance of clear requirements and success metrics - Need for technical expertise in project planning - Value of setting realistic expectations - Risk management in client relationships ## Overall LLMOps Lessons ### Technical Considerations - Importance of proper evaluation frameworks - Need for robust testing and validation - Value of systematic measurement and monitoring - Consideration of multiple architectural approaches ### Organizational Considerations - Clear communication of technical limitations - Importance of setting realistic expectations - Need for proper expertise in project planning - Value of challenging unrealistic requirements ### Implementation Best Practices - Systematic measurement at every stage - Clear documentation and source truth - Proper handling of proprietary data - Robust testing and validation frameworks