Amit Tiwari

Amit Tiwari

Exploring AI, Systems, and Real-World Product Scaling

RAG & Agentic AI

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RAG & Agentic AI

Build intelligent systems that retrieve, reason, and act autonomously to solve complex problems

What You’ll Master

🔍 Retrieval-Augmented Generation (RAG)

Combine AI reasoning with knowledge retrieval

  • RAG Fundamentals - Understanding retrieval-augmented generation principles and benefits
  • Vector Databases - Embedding storage, similarity search, and vector indexing strategies
  • Embedding Models - Choosing and fine-tuning embedding models for your domain
  • Chunk Strategies - Optimal text chunking, overlap, and hierarchical retrieval
  • Hybrid Search - Combining semantic search with keyword-based retrieval

🏗️ RAG Architecture Patterns

Design production-ready RAG systems

  • Basic RAG Pipeline - Document ingestion, embedding, retrieval, and generation
  • Advanced RAG - Multi-step retrieval, re-ranking, and context compression
  • Conversational RAG - Maintaining context across multi-turn conversations
  • Multi-modal RAG - Incorporating images, tables, and structured data
  • Federated RAG - Retrieving from multiple knowledge sources simultaneously

🎯 RAG Optimization Techniques

Maximize retrieval quality and generation accuracy

  • Query Optimization - Query expansion, reformulation, and intent understanding
  • Retrieval Quality - Relevance scoring, re-ranking, and result filtering
  • Context Management - Smart context selection and token budget optimization
  • Hallucination Reduction - Grounding techniques and factual consistency checks
  • Performance Tuning - Latency optimization and cache strategies

🤖 Autonomous AI Agents

Build AI systems that act independently

  • Agent Architectures - ReAct, Chain-of-Thought, and multi-agent frameworks
  • Tool Integration - Connecting agents to APIs, databases, and external systems
  • Planning & Reasoning - Goal decomposition, task planning, and execution strategies
  • Memory Systems - Short-term and long-term memory for persistent context
  • Agent Communication - Multi-agent coordination and collaboration patterns

🔄 Agent Workflows & Orchestration

Design complex multi-step agent processes

  • Workflow Design - Sequential, parallel, and conditional agent execution
  • State Management - Tracking agent state and workflow progress
  • Error Handling - Robust error recovery and graceful degradation
  • Human-in-the-Loop - Integrating human oversight and intervention points
  • Agent Monitoring - Tracking agent performance and decision-making

🛠️ Tool Use & Function Calling

Enable agents to interact with the world

  • Function Calling - Structured tool use and parameter validation
  • API Integration - REST APIs, GraphQL, and real-time data sources
  • Database Queries - Intelligent database interaction and query generation
  • Web Scraping - Dynamic web content extraction and processing
  • File Operations - Document processing, generation, and manipulation

📊 Evaluation & Safety

Ensure agent reliability and safety

  • Agent Evaluation - Task completion rates, accuracy, and efficiency metrics
  • Safety Mechanisms - Guardrails, sandboxing, and permission systems
  • Prompt Injection Defense - Protecting against malicious inputs and commands
  • Audit Trails - Logging agent decisions and actions for accountability
  • Testing Strategies - Unit testing, integration testing, and end-to-end validation

Recent Articles

Coming Soon

Comprehensive RAG and Agentic AI content is in development. Stay tuned!

RAG Implementation Roadmap

🚀 Phase 1: Basic RAG (Weeks 1-2)

Build your first retrieval-augmented system

  • Set up vector database (Pinecone, Weaviate, or Chroma)
  • Implement document chunking and embedding pipeline
  • Create basic retrieval and generation workflow
  • Test with simple Q&A use cases

Deliverables:

  • Working RAG prototype with document ingestion
  • Basic similarity search and answer generation
  • Simple evaluation metrics and testing

📈 Phase 2: Production RAG (Weeks 3-6)

Scale and optimize for production use

  • Implement hybrid search and re-ranking
  • Add conversation memory and context management
  • Build evaluation and monitoring systems
  • Optimize for latency and cost

Deliverables:

  • Production-ready RAG API with monitoring
  • Advanced retrieval with quality scoring
  • Comprehensive evaluation framework

🤖 Phase 3: Intelligent Agents (Weeks 7-12)

Add autonomous reasoning and tool use

  • Implement agent planning and reasoning
  • Connect agents to external tools and APIs
  • Build multi-agent coordination systems
  • Add safety and monitoring mechanisms

Deliverables:

  • Autonomous agents with tool integration
  • Multi-step task planning and execution
  • Safety mechanisms and audit trails

Agent Use Cases & Examples

📚 Knowledge Assistant

Intelligent document Q&A and research

  • Multi-document synthesis and comparison
  • Citation tracking and source attribution
  • Domain-specific knowledge retrieval
  • Research report generation

💼 Business Process Automation

Autonomous workflow execution

  • Lead qualification and routing
  • Customer support ticket triage
  • Data analysis and report generation
  • Compliance monitoring and alerting

🔧 Technical Support Agent

Automated troubleshooting and resolution

  • Log analysis and issue diagnosis
  • Solution recommendation and implementation
  • Knowledge base updates and maintenance
  • Escalation to human experts when needed

📊 Data Analysis Assistant

Intelligent data exploration and insights

  • Natural language query to SQL translation
  • Automated data visualization and reporting
  • Anomaly detection and alerting
  • Predictive analysis and recommendations

Essential Tools & Frameworks

Vector Databases

  • Pinecone - Managed vector database with high performance
  • Weaviate - Open-source vector database with GraphQL API
  • Chroma - Lightweight vector database for prototyping
  • Milvus - Scalable vector database for large-scale deployments

RAG Frameworks

  • LangChain - Comprehensive framework for LLM applications
  • LlamaIndex - Data framework for LLM applications
  • Haystack - End-to-end NLP framework with RAG support
  • Semantic Kernel - Microsoft’s SDK for AI orchestration

Agent Frameworks

  • AutoGPT - Autonomous GPT-4 agent framework
  • LangGraph - State-based agent workflow orchestration
  • CrewAI - Multi-agent collaboration framework
  • AgentGPT - Web-based autonomous agent platform

Embedding Models

  • OpenAI Embeddings - High-quality general-purpose embeddings
  • Sentence Transformers - Open-source embedding models
  • Cohere Embed - Multilingual embedding models
  • BGE Embeddings - BAAI’s general embedding models

Performance Optimization Tips

🎯 Retrieval Quality

  • Chunk Size Optimization - Balance between context and specificity
  • Overlap Strategies - Prevent information loss at chunk boundaries
  • Metadata Filtering - Use document metadata for better relevance
  • Re-ranking Models - Post-process retrieval results for accuracy

⚡ Speed & Efficiency

  • Embedding Caching - Cache frequently accessed embeddings
  • Async Processing - Parallelize retrieval and generation steps
  • Index Optimization - Tune vector database parameters
  • Context Compression - Summarize less relevant context

💰 Cost Management

  • Smart Caching - Reduce redundant API calls and computations
  • Token Optimization - Minimize prompt tokens while maintaining quality
  • Batch Processing - Group operations for efficiency
  • Model Selection - Choose appropriate model size for the task

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