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How do you build RAG systems that work in production?

Most RAG implementations are built to produce answers. The ones that work in high-stakes environments are built to produce answers with calibrated confidence, and to behave differently when confidence is low. The hard engineering work is not retrieval quality. It is teaching the system to know what it does not know.
Tudor Iordache - 18 Jun 2026
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  • What Gartner's 2026 tech trends mean for product teams, not CIOs
    Tudor Iordache - 12 May 2026

    What Gartner's 2026 tech trends mean for product teams, not CIOs

    Of Gartner's ten 2026 technology trends, four matter disproportionately for product builders: AI-native development, multiagent systems, domain-specific language models, and digital trust. AI-assisted development works only when grounded in structured context, not clever prompts. Multiagent systems are already in 80% of enterprise apps shipped in Q1 2026, yet 88% of agent pilots never reach production, because the bottleneck is product design, not model quality. Domain-specific models outperform general ones for targeted use cases, but only when a pre-development business case has set accuracy and cost thresholds. Trust is becoming a visible part of the product surface, and in regulated and European markets it is now a baseline requirement. Teams that win in 2026 will pick the two or three trends that intersect with their roadmap, not try to act on all ten. Gartner published its top strategic technology trends for 2026 last October, presenting ten trends grouped under three themes: The Architect, The Synthesist, and The Vanguard. The recap wave that followed was predictable. Within weeks, dozens of consultancies and vendors had published their own breakdowns, each walking through the same ten trends with broadly similar commentary aimed at the same audience: enterprise CIOs.
    Technology Trending Featured
  • AI document extraction accuracy: why fine-tuning alone is not enough
    Ilie Ghiciuc - 8 May 2026

    AI document extraction accuracy: why fine-tuning alone is not enough

    Document extraction accuracy is not a single problem but a sequence of failure modes resolved in order. Fine-tuning an open-weight visual-language model closes most of the gap from a general-purpose baseline, but rarely the gap that matters: the one between early performance and the threshold a business case requires. Closing that distance is a separate engineering effort, and the techniques that get there compound on each other rather than substitute for each other.
    Technology Featured AI
  • Claude is not a chatbot: how to use it on live product builds
    Ionut Lomer - 29 Apr 2026

    Claude is not a chatbot: how to use it on live product builds

    Most teams have adopted AI assistants in some form by now. Few have built systems around them. A single conversation with Claude is transactional. You type a question, you get an answer, and the session ends. That has real value, but it scales like a calculator: only as fast as you type. A system built with Claude is different. Context is loaded before the work starts. The model knows the project methodology, the team constraints, and the deliverable format before a single instruction is given. Output is consistent across sessions. The re-briefing tax disappears.
    Technology Team Featured AI
  • How regulated companies build AI without sending data to third-party APIs
    Ilie Ghiciuc - 22 Apr 2026

    How regulated companies build AI without third-party APIs

    At some point in almost every AI project we work on with a regulated client, someone on the team says some version of the same thing: "We can't send that data outside." It usually lands like a problem. The obvious implementation path, connect your data to a capable hosted model, get results back, iterate, suddenly has a wall across it. SOC 2 compliance, data residency requirements, security perimeters built around credentials and sensitive client information: any of these can make the default approach to AI architecture a compliance violation rather than a technical decision.
    Featured AI Energytech Architecture
  • Edge computing vs. cloud computing: how to choose your architecture
    Tudor Iordache - 16 Apr 2026

    Edge computing vs. cloud computing: how to choose your architecture

    The edge vs. cloud decision is a workload classification problem, not a technology preference. Five factors determine placement: latency tolerance, data volume, compliance requirements, resilience needs and infrastructure cost. Misallocation in either direction carries measurable consequences, defaulting to cloud accumulates latency debt and egress costs at scale, while premature edge investment introduces operational complexity before the business case is established. Organisational readiness is an independent variable that shapes when edge adoption is viable, separate from whether it is technically warranted.
    Technology Featured Cloud Architecture
  • How to build a business case for AI before writing a line of code
    Stefan Sarbu - 6 Apr 2026

    How to build a business case for AI before writing a line of code

    A pre-development business case for an AI initiative functions as a technical constraint document: the cost of what is displaced, the accuracy threshold required to sustain that displacement, and the maximum allowable cost per unit of output together determine architectural decisions before any model is evaluated. Without these constraints defined in advance, accuracy targets become intuitive, infrastructure choices become arbitrary, and the decision to move to production becomes a matter of engineering preference rather than measurable evidence. The sequencing discipline, business case before technical evaluation, is what makes success criteria testable and completion definable.
    Featured AI
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