Agentic AI meets DevOps. Building autonomous, cloud-native systems that think, act, and scale.

6. GraphRAG — Multi-Hop Reasoning with a Local Knowledge Graph

Go beyond flat vector search. Build a knowledge graph from your documents, run multi-hop queries that traverse entity relationships, and combine graph traversal with vector retrieval for richer answers.

5. Evaluating RAG Quality with RAGAS

Stop guessing whether your RAG pipeline is good. Use RAGAS to measure faithfulness, answer relevancy, context precision and recall — and learn to interpret the metrics to drive concrete improvements.

4. Streaming RAG Chat — FastAPI SSE + React

Add token-by-token streaming to your RAG pipeline. Server-Sent Events from FastAPI, a React client with AbortController, and the nuances of streaming structured retrieval responses.

3. Multi-Tenant Document Q&A API

Build a multi-tenant RAG API where each tenant's documents are fully isolated. FastAPI, per-tenant ChromaDB collections, JWT authentication, and a clean REST interface for document ingestion and querying.

2. Build Your First RAG System from Scratch

Strip away every framework and build RAG with raw Python — numpy cosine similarity, direct OpenAI calls, no LangChain. Understand exactly what the abstractions hide before you use them.

1. Document Analysis with LLMs

Build a production-ready document Q&A pipeline from scratch. Load PDFs, Word files and web pages, split them into semantic chunks, embed with open-source models, store in a vector DB, and answer questions with full source attribution — zero paid APIs required.

Your AI Agent Works Perfectly in the Demo. Here Are the 6 Ways It Dies in Production with AI Automation

AI agents rarely fail in demos - they fail in production. Discover the 6 critical reasons AI automation systems break at scale: model drift, weak governance, poor observability, data quality issues, scalability bottlenecks, and security gaps.

Deploying a Multistage Multimodal Recommender System on Amazon Elastic Kubernetes Service

Deploy a scalable multistage multimodal recommender system on Amazon Web Services using Amazon Elastic Kubernetes Service. Optimize inference pipelines, autoscaling, and GPU workloads for high-performance personalized recommendations.

OpenAI Introduces Websocket-Based Execution Mode to Reduce Latency in Agentic Workflows

OpenAI has introduced a WebSocket-based execution mode designed to reduce latency in agentic AI workflows by enabling faster, persistent bidirectional communication between agents and execution environments.

Inference Scaling (Test-Time Compute): Why Reasoning Models Raise Your Compute Bill

Reasoning models use more compute at inference time by exploring multiple paths, evaluating options, and refining outputs. This increases token usage, latency, and overall cost compared to standard single-pass models.

Mistral AI Introduces Workflows for Orchestrating Enterprise AI Processes

Your AI models are capable. Your production deployments are not. Mistral's Workflows addresses the infrastructure gap that kills every enterprise AI pilot — built on the same durable execution engine trusted by Netflix, Stripe, and Salesforce.

Designing Cognitive Memory for AI Agents

AI agents fail at continuity due to stateless design. LinkedIn’s Cognitive Memory Agent (CMA) introduces persistent, structured memory—episodic, semantic, procedural - enabling agents to retain context, adapt over time, and move from prompt-driven responses to truly stateful intelligence.