Structured observations from specialists working directly with language model architecture, dialogue systems, and enterprise deployment. Each piece addresses a specific challenge — not a general overview.
The articles here come from professionals who have encountered real constraints in production environments: latency tradeoffs, context window management, fallback logic, and user expectation calibration.
A look at what actually shifted in conversational AI development when context limits expanded from 4K to 128K tokens, and what that means for how systems behave now.
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How the role of fine-tuning in conversational AI shifted from the primary customization method to something more surgical — and why that changes the build process.
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Retrieval-augmented generation sounds straightforward until you build one. Here is what actually happens behind the scenes and where things tend to break.
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The model is the easy part. Deciding what a conversational AI system should refuse, soften, or redirect requires judgment calls that no framework makes for you.
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Conversational AI systems lose character coherence over long sessions in measurable ways. Here is what causes it and how builders address it in practice.
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Accuracy scores tell you almost nothing about whether a conversational AI works for real users. Here is what evaluation actually looks like in practice.
Read articleEach piece submitted to this collection goes through a structured editorial process before it appears here. The goal is specificity — articles that describe what actually happened in a given system, not what should have happened in theory.
Contributors submit a draft along with a brief description of the project context — the type of system, the environment, and the specific problem they encountered. Submissions without context are returned.
A second practitioner reads the draft and checks whether the claims hold up against their own experience. Disagreements go back to the author with specific questions, not general edits.
The editorial team removes abstractions and forces the author to replace vague language with concrete references — tools used, parameters tested, outcomes observed without exaggeration.
Articles appear with the author's stated role and the system type they were working on. No credentials without specifics — readers should be able to judge relevance themselves.
Articles collected here are not product announcements or vendor comparisons. Plav Darsen operates as a transnational group practice, and the writing reflects that — practitioners from different countries, working on different deployment types, reporting what they observed without aligning to a single vendor's narrative.
The diversity of contexts is intentional. A constraint that matters in one region's regulatory environment may be irrelevant elsewhere. Contributors are expected to be specific about where their observations apply.