Essential Tools for Genai Projects

If you’re an AI engineer building a full-stack GenAI application, this one’s for you. The open agentic stack has evolved. It’s no longer just about choosing the “best” foundation model. It’s about designing an interoperable pipeline, from serving to safety- that can scale, adapt, and ship. Let’s break it down 👇 🧠 1. Foundation Models Start with open, performant base models. → LLaMA 4 Maverick, Mistral‑Next‑22B, Qwen 3 Fusion, DeepSeek‑Coder 33B These models offer high capability-per-dollar and robust support for multi-turn reasoning, tool use, and fine-grained control. ⚙️ 2. Serving & Fine-Tuning You can’t scale without efficient inference. → vLLM, Text Generation Inference, BentoML for blazing-fast throughput → LoRA (PEFT) and Ollama for cost-effective fine-tuning If you’re not using adapter-based fine-tuning in 2025, you’re overpaying and underperforming. 🧩 3. Memory & Retrieval RAG isn’t enough, you need persistent agent memory. → Mem0, Weaviate, LanceDB, Qdrant support both vector retrieval and structured memory → Tools like Marqo and Qdrant simplify dense+metadata retrieval at scale → Model Context Protocol (MCP) is quickly becoming the new memory-sharing standard 🤖 4. Orchestration & Agent Frameworks Multi-agent systems are moving from research to production. → LangGraph = workflow-level control → AutoGen = goal-driven multi-agent conversations → CrewAI = role-based task delegation → Flowise + OpenDevin for visual, developer-friendly pipelines Pick based on agent complexity and latency budget, not popularity. 🛡️ 5. Evaluation & Safety Don’t ship without it. → AgentBench 2025, RAGAS, TruLens for benchmark-grade evals → PromptGuard 2, Zeno for dynamic prompt defense and human-in-the-loop observability → Safety-first isn’t optional, it’s operationally essential 👩💻 My Two Cents for AI Engineers: If you’re assembling your GenAI stack, here’s what I recommend: ✅ Start with open models like Qwen3 or DeepSeek R1, not just for cost, but because you’ll want to fine-tune and debug them freely ✅ Use vLLM or TGI for inference, and plug in LoRA adapters for rapid iteration ✅ Integrate Mem0 or Zep as your long-term memory layer and implement MCP to allow agents to share memory contextually ✅ Choose LangGraph for orchestration if you’re building structured flows; go with AutoGen or CrewAI for more autonomous agent behavior ✅ Evaluate everything, use AgentBench for capability, RAGAS for RAG quality, and PromptGuard2 for runtime security The stack is mature. The tools are open. The workflows are real. This is the best time to go from prototype to production. ----- Share this with your network ♻️ I write deep-dive blogs on Substack, follow along :) https://lnkd.in/dpBNr6Jg

Agentic AI Is Promising, But RAG Has Been Doing the Heavy Lifting for Years While Agentic AI continues to evolve, it's Retrieval-Augmented Generation (RAG) that has powered some of the most practical, production-ready AI applications over the past 2–3 years. From enterprise search to chatbots, copilots, and domain-specific QA systems—RAG is the backbone of many GenAI solutions in use today. To help navigate this growing ecosystem, here’s a breakdown of the modern RAG Developer Stack, covering all critical components: ⫸ LLMs – Open-source (e.g., LLaMA 3, Mistral, Qwen) and proprietary (e.g., OpenAI, Claude, Gemini) ⫸ Frameworks – LangChain, LlamaIndex, Haystack, Txtai ⫸ Vector Databases – Chroma, Pinecone, Qdrant, Weaviate, Milvus ⫸ Data Extraction – Tools for web and document ingestion like Crawl4AI, MegaParser, Docling ⫸ Text Embeddings – Open (SBERT, Ollama) and closed (OpenAI, Cohere, Gemini) ⫸ Open LLM Access – Groq, Together AI, Hugging Face, Ollama ⫸ Evaluation Tools – Giskard, Ragas, Trulens for observability, feedback loops, and trust Each layer plays a critical role—from reducing hallucinations to improving latency and enabling real-time responses. ➤ Which part of the RAG stack do you find most challenging or exciting to work with?

How do you navigate the complex ecosystem of Generative AI applications? Generative AI is revolutionizing industries, but building impactful applications requires a deep understanding of the tools and infrastructure that power them. To help simplify this, I’ve mapped out the Generative AI Application Ecosystem—a comprehensive overview of how the pieces fit together. Here’s a detailed breakdown of the key components: 1. Frontend: Where Users Interact • App Hosting: Platforms like Vercel and Streamlit make it easy to deploy and manage user-facing applications. • Chatbots and Playgrounds: Frameworks like Amazon Lex enable dynamic user interactions. • Orchestration: Tools like LangChain and LlamaIndex streamline the integration of various Generative AI components. 2. Backend: The Core Engine • LLMs APIs and Hosting: • Open-source models (e.g., Hugging Face, Replicate) provide flexibility. • Proprietary APIs (e.g., OpenAI, AI21 Labs) deliver state-of-the-art capabilities. • ML Infrastructure: Built on cloud providers (e.g., AWS, GPU instances) for scalable and efficient computation. • LLMCache: Tools like Redis and GPTCache optimize performance and reduce latency. • MLOps and Monitoring: Frameworks like Weights & Biases and SageMaker ensure reliable deployment and monitoring of AI models. 3. Tools: Enhancing the Workflow • Embedding Models/VectorDB: Pinecone, FAISS, and Weaviate offer fast and accurate search capabilities. • Validation Frameworks: Tools like Nemo-Guardrails and ConstitutionalChain ensure outputs are trustworthy and safe. • Developer Tools and Plugins: APIs and performance metrics help refine applications and enhance usability. • Annotations/RLHF: Reinforcement learning techniques are critical for improving AI responses. Why this ecosystem matters: Understanding these interconnected layers enables developers, data scientists, and product teams to design, deploy, and monitor robust Generative AI applications that can scale to meet user needs. What tools or frameworks have you found invaluable in your journey with Generative AI? Let’s discuss in the comments! Join our Newsletter with 137000+ followers — https://lnkd.in/dbZPj6Tu Follow me for more detailed insights like this. #data #ai #agents #theravitshow

When I started in #product 15 years ago, everyone used Jira, Confluence, Balsamiq, and later Airtable + Figma. With GenAI, the landscape has evolved, and here is a list of tools I expect every PM to use to stay ahead. ✨ Strategy & Competitor Analysis ✨ I think #Notion did a great job upgrading their capabilities and integrating OpenAI and Anthropic models (not sure what #Coda is doing?), which support drafting and refining strategies using internal (Slack messages, docs) and external data (investor presentations, etc.). I have personally used #Competely, which provides a massive head start and notifies you when competitors release new features and their potential impact on your strategy. 🔎 Customer Research & Discovery 🔎 Platforms such as #Kraftful automate feedback aggregation from various sources. Pushing it further, #Genway creates agents that automatically conduct your interviews, and #NextMinder can simulate research based on provided customer segment details and behavior, allowing you to simulate millions, not just dozens, of users. 🚀 Rapid Prototyping 🚀 Much has been said here, and tools like #Loveable are growing at a rapid pace. However, I’m personally more of a fan of the #Uizard toolkit, which lets you upload screenshots and whiteboard drafts and turn them into mobile and desktop designs automatically (and can also generate functional code). ✏️ Requirements & User Stories✏️ I think every PM has now used ChatGPT to generate requirements or user stories. I’ve personally found more success with #Claude, and investing in building your own GPT, populated with your strategy context, OKRs, and example PRDs and user stories, goes a long way. ✅ Testing & Validation ✅ I started product when we forced PMs to write Gherkin syntax into user stories. #QualGent and #Spur are two great examples on how Agents + MCP will change the way Product Managers will test software before it reaches users. 🤝 Collaboration & Documentation 🤝 I haven’t used them in action yet, but #Quantstruct and #Mem are notes on steroids: they automatically feed into a central knowledge base accessible by the team and help automate documentation. I’m eager to see how far we can push this in the context of technical/API/feature documentation and how we can remove outdated content from it. #GenAI #ProductManagement Shivani Rathi, Emily Gao, Shai Dinnar, Dimitrios Lippe, Bradley Antcliff, Frederic Doppstadt