Lecture on AI and Knowledge Management

Introduction to AI in Knowledge Management

• AI offers significant value in Knowledge Management (KM).
• Most organizations have extensive documentation and meeting notes.
• Humanly impossible to process and stay updated with all the data effectively.
• Large Language Models (LLMs) can read and retrieve information efficiently.
• Growing trend of AI-based platforms for KM, e.g., chatbots for PDFs, PowerPoints, and spreadsheets.

Search Engines and AI Disruption

• Discussion on potential disruption of traditional search engines (e.g., Google) by LLMs.
• LLMs can provide personalized answers, reducing the need for traditional search queries.
• Increasing popularity of platforms like ChatGPT for answering questions.
• Specialized platforms for corporate KM are emerging (e.g., LinkedIn).

Building Reliable AI Applications

• Exploring what's working and what's not in AI applications for business use cases.
• Two common ways to incorporate knowledge into LLMs:
  1. Fine-tuning/Training: Embedding knowledge directly into model weights.
     • Fast inference but complex to manage and requires proper training data.
  2. In-context Learning/Retrieval Augmented Generation (RAG): Adding knowledge as part of the prompt.
     • Retrieves relevant data from databases and adds it to the prompt context.

Setting Up RAG Pipelines

• Start with data preparation: Extract information and convert it into a vector database.
• Vector databases understand semantic relationships between data points.
• RAG involves retrieving relevant information for user questions and adding it to the prompt context.

Challenges in Implementing RAG

• Real-world data is messy and diverse (e.g., images, diagrams, charts in PDFs).
• Accurate data extraction is critical yet challenging.
• Different data types require different retrieval methods (e.g., vector search vs. keyword search).
• Example: Sales data queries involve data from multiple sources and require pre-calculation.
• The complexity of real-world KM use cases makes simple RAG implementations insufficient.

Advanced RAG Techniques

• Better data parsing can significantly improve quality (e.g., tools like Llama parser for PDFs).
• Different parsing tools for different document types (e.g., FireCrawl for websites).
• Unified data format (markdown) optimizes RAG pipelines.

Optimizing Data Extraction

• Format documents into small chunks: Essential for mapping data into vector space for retrieval.
• Balancing chunk size for optimal performance (too big or too small chunks impact performance).
• Experiment to find optimal chunk sizes for different document types.

Retrieval Accuracy Improvements

• Use ranking models to re-rank retrieved documents based on relevance.
• Hybrid search: Combines vector and keyword searches for better accuracy.

Introduction to Agentic RAG

• Agents decide the optimal RAG pipeline and perform self-checks to enhance answer quality.
• Query translation: Modify user questions to be more retrieval-friendly (e.g., abstract complex queries).
• Metadata filtering: Use metadata to filter database searches, increasing result relevance.

Self-Reflection and Correction in RAG

• Self-reflection processes improve RAG accuracy (e.g., corrective RAG agent for high-quality results).
• Multi-step validation processes ensure answers are relevant and non-hallucinated.
• Tools like LangGraph and TBG for implementing corrective RAG agents on local machines.

Conclusion

• Careful consideration and implementation of advanced RAG techniques improve reliability and accuracy of AI applications in KM.
• Encourage experimentation and sharing of effective RAG tactics.
• Continuous posting of AI projects for community learning and engagement.