Contextual AI

If you're in the Boston area this week, make time for "Pioneering the New Era with AI" event, organized by Imagination in Action, Massachusetts Institute of Technology, and Forbes! Catch Douwe Kiela and other AI leaders as they share dialogue around the transformative future and practical considerations for AI today. 🌟Event Details: April 15, 2025 | 7 AM – 7 PM 📍 MIT Media Lab, 75 Amherst Street, Cambridge, MA 🔗 Event Website: https://lnkd.in/dQh-qc4U 🔗 Full Summit Schedule: https://lnkd.in/gqzJ9WZ7

Ever wish you could connect Claude, Cursor, or any LLM to your internal RAG without wrangling a bunch of custom code? We make that super easy using an MCP Server and with Contextual AI RAG agents. 🧠 Why MCP? MCP (Model Context Protocol) acts as a lightweight abstraction layer that lets your LLMs call external tools easily. Instead of direct API calls, you define tools once in your MCP server and just use them anywhere. What I Show in the Video: 💸 Query financial RAG agents inside Clause Desktop 💻 Pull code snippets from technical docs in Cursor 🧩 Server logic stays clean and flexible — all in one config with MCP ☁️🖥️ Deploy it locally or in the cloud, and you’re off to the races You can extend this MCP Server to: 🔁 Chain agents ⚙️ Change prompts/retrievers 📊 Pull metrics …all using the same lightweight interface Next steps: Watch my video and reach out if you have questions Check out the github repo to try Signup for Contextual AI!

"RAG is dead. Long live RAG." Every time a new model drops with an expanded context window (like Meta's impressive Llama 4 Scout with its 10M token capacity), I see the inevitable "RAG is dead" posts flooding my feed. But this fundamentally misunderstands what RAG is about. When we developed RAG five years ago, we weren't creating a workaround for small context windows—we were designing a principled approach to augment models with external knowledge. The core enterprise challenges RAG addresses remain unsolved with just larger context windows: • Accessing private data and knowledge • Overcoming outdated knowledge • Reducing hallucinations and providing strong attributions The most sophisticated AI systems don't choose between either RAG, or long context, or fine-tuning, or MCP—they strategically combine these complementary approaches. Stop believing in false dichotomies. 👇 Read my full thoughts in the blog post linked in the comments.

Top AI researcher on DeepSeek Listen to the full episode with Douwe Kiela on: YouTube: https://lnkd.in/gWUSYwTv Spotify: https://lnkd.in/gchdZN2v Apple Podcasts: https://lnkd.in/gTamnv94

We're excited to share that Elizabeth Lingg, Director of Applied Research at Contextual AI, will be speaking at #ECIR25 in Tuscany this week on “The DNA of Enterprise AI: Architecting Systems for Agentic RAG through Specialization”. 📍 Don’t miss it! https://lnkd.in/gr3Ye2nR

Go behind the scenes with Contextual AI CTO Amanpreet Singh as he breaks down the future of enterprise AI with Daniel Darling. You'll learn about grounded LLMs, specialized AI agents, and what’s next for AI in the enterprise—read the full article: https://dub.sh/NHryEMB

🔥 Introducing the most reliable way to evaluate LLMs and agents in production! It's time to stop “vibe testing” your AI systems. Our latest developer's guide shows you how to rigorously test AI systems so that they hold up in production, using Contextual AI's LMUnit evaluation model and CircleCI's CI/CD pipeline. You’ll learn how to: • Write natural language unit tests that anyone on your team can understand • Leverage LMUnit – Contextual AI's state-of-the-art, specialized evaluation language model that outperforms frontier models with greater interpretability at lower cost  • Implement CircleCI's CI/CD pipeline to catch regressions before they reach users See our complete developer’s guide here: https://lnkd.in/gCHfZ575 Stop relying on "vibes" and start building AI you can trust! #AITesting #LLMOps #DevOps #Agents #LLM #Evaluation

🔥 Introducing the most reliable way to evaluate LLMs and agents in production! It's time to stop “vibe testing” your AI systems. Our latest developer's guide shows you how to rigorously test AI systems so that they hold up in production, using Contextual AI's LMUnit evaluation model and CircleCI's CI/CD pipeline. You’ll learn how to: • Write natural language unit tests that anyone on your team can understand • Leverage LMUnit – Contextual AI's state-of-the-art, specialized evaluation language model that outperforms frontier models with greater interpretability at lower cost  • Implement CircleCI's CI/CD pipeline to catch regressions before they reach users See our complete developer’s guide here: https://lnkd.in/gCHfZ575 Stop relying on "vibes" and start building AI you can trust! #AITesting #LLMOps #DevOps #Agents #LLM #Evaluation

Don't miss this in-depth conversation between Amanpreet Singh and Daniel Darling on the latest episode of the Five Year Frontier. Available wherever you get your podcasts: - YouTube: https://lnkd.in/g4zkVDze - Apple: https://dub.sh/7ec9DBB - Spotify: https://dub.sh/5tnDZZV

Heading to Google Cloud Next 25 in Las Vegas next week?
We’ll be there! Come and meet the Contextual AI team in the AI Orchestration Area at Booth SA13 to learn how you can build specialized RAG agents. Plus, join AI Engineer Rajiv Shah on April 10 at 5:00 PM for an expert panel on Startup genAI: Proof of concept to production. See you in Vegas!