Github repo: https://t.co/JHLsO3My9u(don't forget to star it ⭐) ...

LLM Security Challenges - LLMs face adversarial attacks via prompts, requiring focus on security beyond correctness, faithfulness, and factual accuracy [1] - A well-crafted prompt can lead to PII leakage, bypassing safety filters, and generating harmful content [2] - Red teaming is core to model development, demanding SOTA adversarial strategies like prompt injections and jailbreaking [2] Red Teaming and Vulnerability Detection - Evaluating LLM responses against PII leakage, bias, toxic outputs, unauthorized access, and harmful content generation is crucial [3] - Single-turn and multi-turn chatbots require different tests, focusing on immediate jailbreaks versus conversational grooming, respectively [3] - DeepTeam, an open-source framework, performs end-to-end LLM red teaming, detecting 40+ vulnerabilities and simulating 10+ attack methods [4][6] DeepTeam Framework Features - DeepTeam automatically generates prompts to detect specified vulnerabilities and produces detailed reports [5] - The framework implements SOTA red teaming techniques and offers guardrails to prevent issues in production [5] - DeepTeam dynamically simulates adversarial attacks at run-time based on specified vulnerabilities, eliminating the need for datasets [6] Core Insight - LLM security is a red teaming problem, not a benchmarking problem; thinking like an attacker from day one is essential [6]

RT Avi Chawla (@_avichawla)30 Agentic AI terms AI engineers should know: https://t.co/uOnNZEXsu0 ...

30 Agentic AI terms AI engineers should know: https://t.co/uOnNZEXsu0 ...

RT Avi Chawla (@_avichawla)RAG vs. Graph RAG, explained visually!RAG has many issues.For instance, imagine you want to summarize a biography, and each chapter of the document covers a specific accomplishment of a person (P).This is difficult with naive RAG since it only retrieves the top-k relevant chunks, but this task needs the full context.Graph RAG solves this.The following visual depicts how it differs from naive RAG.The core idea is to:- Create a graph (entities & relationships) from documents.- Trave ...

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs. https://t.co/CVUW8FVKgjAvi Chawla (@_avichawla):RAG vs. Graph RAG, explained visually!RAG has many issues.For instance, imagine you want to summarize a biography, and each chapter of the document covers a specific accomplishment of a person (P).This is difficult with naive RAG since it only retrieves the top-k relevant https://t.co/Ad5ztdo7Lz ...

Technology & Software Development - Graph RAG is presented as a practical example for RAG over code, addressing limitations of naive chunking in handling codebases with long-range dependencies [1] - Graph-Code, a graph-driven RAG system, is introduced for analyzing Python codebases and enabling natural language querying [1] - Graph-Code extracts classes, functions, and relationships from code through deep code parsing [1] - Memgraph is utilized to store the codebase as a graph within the Graph-Code system [1] - Graph-Code parses pyproject files to understand external dependencies [1] - The system retrieves actual source code snippets for found functions [1]

RAG System Architecture on AWS - RAG (Retrieval Augmented Generation) is a two-stage pattern involving ingestion and querying [1][3] - The architecture remains consistent even with enhancements like better chunking, smarter retrieval, caching, orchestration, streaming, eval pipelines, or multi-source ingestion [3] Ingestion Process - Raw data from sources like S3 is processed and chunked by a Lambda function [3] - Chunks are embedded using Bedrock Titan Embeddings [3] - Embeddings are stored in a vector database such as OpenSearch Serverless, DynamoDB, or Aurora, creating a searchable knowledge store [3] - Strategic reindexing with smart diffing, incremental updates, and metadata checks is important for efficiency and cost reduction [2] Querying Process - User questions are processed through API Gateway to a Lambda function [4] - Questions are embedded using Bedrock Titan Embeddings [4] - The embedding is matched against the vector database to retrieve relevant chunks [4] - Retrieved chunks are passed to a Bedrock LLM (Claude or OpenAI) to generate the final answer [4]

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs. https://t.co/pxlp7JJJ4VAvi Chawla (@_avichawla):How to build a RAG app on AWS!The visual below shows the exact flow of how a simple RAG system works inside AWS, using services you already know.At its core, RAG is a two-stage pattern:- Ingestion (prepare knowledge)- Querying (use knowledge)Below is how each stage works https://t.co/YcTgvXbJlb ...

8 RAG architectures all AI Engineers should know: https://t.co/InQh2mJxO3 ...