RAG 2.0: Structured, Self-Aware, Governed Retrieval Retrieval-Augmented Generation (RAG) helps language models answer using your data instead of guessing. RAG 1.0 works, but it is a modular pipeline that can break in production. RAG 2.0 treats retrieval and generation as one system, adds smarter retrieval behavior, and makes compliance checks part of the flow. Get in Touch What is RAG? RAG is a setup where a language model pulls relevant documents at answer time, then uses that retrieved context to write a response. The goal is simple: fewer made-up answers and better alignment with what your sources actually say.
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Reasoning-Focused LLMs & Test-Time Compute New “reasoning” language models don’t just answer… they work through the steps. The big shift is happening at inference time: models spend more compute to try, check, and refine. This deep dive breaks down what that means, why it helps (especially for math, code, and logic), and what you pay for the improvement. Start Reading What’s going on with “reasoning models”? A fun example: ask an AI how many “R” letters are in strawberry. Older models might guess. Reasoning-centric models will often spell it out and count. That step-by-step behavior is the point. It’s
The Current State of Multimodal Video Generation Text to video has moved past “cool demo” territory. The real leap is that control, realism, and audio are landing together. This report breaks down what changed, how OpenAI’s Sora 2 compares to Google’s Veo 3.1, where teams are using these tools today, and what still needs work. Get in Touch Multimodal generation in plain English Multimodal generation is when a model can create across formats like text, images, audio, and video. Video is the hardest one to get right because it is not a single output. It is a sequence of frames
Small & Open-Weight Models Are Catching Up The performance gap between closed giants and open-weight models is shrinking fast. What’s changing the game is not just benchmark scores. It’s the combo of strong accuracy, much lower inference cost, and the ability to run and tune models on your own hardware. Jump to the TL;DR TL;DR Open-weight and smaller models (think Mistral 7B, Phi-2, Gemma, TinyLlama, and Mixtral) are now competitive on a lot of the benchmarks people actually care about: knowledge, reasoning, coding, and math. Closed models still lead at the very top end, but for many real products
Securing LLMs in Production LLMs make products feel magical, right up until someone realizes your chatbot can be manipulated with plain English. The new attack surface is the model’s behavior: what it will reveal, what it will believe, and what it can be tricked into doing. This page breaks down the real threats (prompt injection, data leakage, model theft, supply chain risks) and the platform options that help you defend them. Get in Touch Why LLM security is different Classic app security assumes your code follows rules. LLMs follow instructions, including instructions hidden inside documents, web pages, and chat
AI-Assisted Software Engineering: From Autocomplete to Autonomous Agents Code generation has moved past “help me type faster.” The newest tools can read a repo, change multiple files, run tests, and open pull requests. This report breaks down what changed, what’s real today, and what engineering teams should do to stay in control as output volume ramps up. Start Introduction AI-assisted coding has evolved fast. What started as autocomplete (predict the next token, line, or snippet) has turned into systems that can tackle full software tasks with minimal prompting. In surveys, most developers now use or plan to use AI
Top 10 Signs Your Competitors Are Ahead of You in AI (and How to Catch Up in Retail) AI is changing retail fast. 42% of retailers have already adopted AI and another 34% are running pilot programs. Some retailers using advanced AI have been growing dramatically faster than their competitors. If you want a quick, practical way to spot where the gap is opening, start here. Jump to the 10 signs Why this matters In retail, AI leadership usually does not look like a flashy demo. It shows up as less friction for customers and fewer headaches for teams:
Top 20 AI Predictions for 2026 This is a practical, trend-driven list of what leaders expect to become real in 2026: enterprise adoption, agentic workflows, governance, education, healthcare, finance, and the public pushback that’s already starting to build. Jump to the predictions What you’re looking at These 20 predictions are grounded in current enterprise behavior and what major analysts and operators are putting their names behind. It’s less “science fiction” and more “what shows up in your budget, your org chart, and your risk reviews.” I kept the explanations short enough to scan, but specific enough that you can
You tweak a prompt. Upgrade a model. Swap a tool. Suddenly the ad generator starts missing character limits or the SEO brief wanders off-brand. No one notices until the campaign is live. Guesswork is the default. It doesn’t have to be. Treat your AI workflow like software: snapshot the correct behavior, then automatically compare every new run against that snapshot before you release. Read More AI Articles Key concepts Golden outputs are the “this is correct” snapshots for a small but representative set of inputs. Fixtures are the saved inputs and context your workflow expects. Regression
As AI features move from experiments to production, two things start to bite: cost drift and opaque failures. The fix is not “more dashboards.” It’s an operating model: instrument every step, enforce token budgets, design caches that won’t burn you, and make errors useful for both developers and users. Read More AI Articles 1) Observe the whole flow Make every request traceable from the first byte to the last token. Minimum structured event per request Correlation ID and user/tenant ID. Model, version, parameters, tool list, temperature, top_p. Prompt token count, completion token count, total tokens. Estimated