What's included

• RAG System Development

  Design and implement Retrieval-Augmented Generation (RAG) systems for various natural language processing tasks Integrate state-of-the-art language models (e.g., GPT-4, Claude) with retrieval components for enhanced performance Optimize retrieval recall by extracting metadata from structured and unstructured data sources

• Agentic Workflow Integration

  Develop agentic workflows to enhance the reliability and performance of language models Implement techniques for task decomposition, multi-step reasoning, and iterative refinement Leverage agentic workflows to improve language model capabilities across a wide range of applications

• Large Language Model (LLM) Integration

  Seamlessly integrate and fine-tune powerful language models like GPT-4 and Claude into your applications Develop custom prompting techniques and prompting strategies for optimal performance Implement safeguards and controls to ensure responsible and ethical use of LLMs

• Performance Optimization and Monitoring

  Continuously monitor and analyze the performance of RAG systems and LLM integrations Implement techniques for performance tuning, error analysis, and model debugging Develop strategies for handling edge cases, mitigating biases, and improving overall system reliability

• Documentation and Knowledge Transfer

  Provide comprehensive documentation on system architecture, implementation details, and best practices Conduct knowledge transfer sessions to ensure smooth handover and ongoing maintenance