Advanced Retrieval-Augmented Generation (RAG)in Bengaluru

Module 1: Advanced RAG Architecture

• Understanding advanced Retrieval-Augmented Generation concepts and enterprise RAG workflows in BengaluruIndia
• Advanced RAG architecture and core components
  • Retrieval, ranking, context construction, and generation layers
  • Designing modular and scalable RAG pipelines
• Limitations of basic RAG systems
  • Retrieval gaps and irrelevant context
  • Context-window and hallucination challenges

Module 2: Advanced Document Processing

• Advanced document ingestion and knowledge preparation in BengaluruIndia
• Processing complex data sources
  • PDFs, web pages, structured data, and enterprise documents
  • Metadata extraction and document enrichment
• Data preprocessing techniques
  • Cleaning and normalization
  • Duplicate detection and document quality management

Module 3: Advanced Chunking and Contextual Retrieval

• Advanced text chunking strategies for improved retrieval in BengaluruIndia
• Chunking approaches
  • Semantic and recursive chunking
  • Hierarchical and structure-aware chunking
• Contextual retrieval techniques
  • Preserving document relationships
  • Improving contextual understanding across chunks

Module 4: Embeddings and Advanced Vector Search

• Advanced embedding techniques for high-quality semantic retrieval in BengaluruIndia
• Embedding models
  • Selecting embedding models for different use cases
  • Understanding embedding dimensions and similarity
• Vector search optimization
  • Similarity metrics
  • Indexing strategies
  • Metadata filtering

Module 5: Hybrid Search and Reranking

• Implementing advanced retrieval strategies for RAG applications in BengaluruIndia
• Hybrid search
  • Combining keyword and semantic search
  • Balancing lexical and vector-based retrieval
• Reranking techniques
  • Cross-encoder and relevance-based reranking
  • Improving retrieved context quality

Module 6: Query Optimization and Retrieval Strategies

• Optimizing user queries for improved information retrieval in BengaluruIndia
• Query transformation
  • Query expansion and rewriting
  • Multi-query retrieval
• Advanced retrieval methods
  • Parent-child retrieval
  • Metadata-aware retrieval
  • Multi-step retrieval workflows

Module 7: Advanced RAG with LLMs

• Integrating advanced retrieval workflows with Large Language Models in BengaluruIndia
• Context management
  • Context selection and compression
  • Reducing irrelevant information
• LLM response generation
  • Prompt design for retrieved context
  • Grounded and context-aware response generation

Module 8: RAG Evaluation and Optimization

• Evaluating and improving advanced RAG system performance in BengaluruIndia
• Retrieval evaluation
  • Context relevance
  • Retrieval accuracy and recall
• Generation evaluation
  • Response relevance
  • Faithfulness and groundedness
• RAG optimization
  • Identifying retrieval and generation bottlenecks
  • Improving overall system performance

Module 9: Production and Enterprise RAG

• Understanding production considerations for enterprise RAG solutions in BengaluruIndia
• Scalable RAG architecture
  • Performance and latency considerations
  • Data and knowledge-source management
• Security and responsible AI
  • Access control and data privacy
  • Reducing sensitive-data exposure
  • Responsible use of enterprise knowledge

Module 10: Advanced RAG Project and Future Trends

• Developing an advanced RAG application using an end-to-end workflow in BengaluruIndia
• Practical implementation
  • Data ingestion and advanced chunking
  • Embeddings and vector search
  • Hybrid retrieval and reranking
  • LLM integration and response generation
• Project evaluation and optimization
  • Testing retrieval quality
  • Evaluating generated responses
  • Troubleshooting and improving the RAG pipeline
• Future of RAG
  • Agentic RAG and multimodal RAG
  • Enterprise AI and advanced LLM applications
  • Emerging RAG architectures and technologies