Samuel Duvains sits down with Vijay Raina, a specialist in enterprise SaaS technology, software design, and architecture. Vijay has helped multiple engineering organizations adopt AI while protecting quality, safety, and velocity. In this conversation, he unpacks why developer trust is sliding even as usage climbs, why “almost right” code is a trap, and how human validation loops and curated knowledge spaces are becoming the backbone of AI at work. We explore practical playbooks—standing up RAG grounded in reliable sources, structuring metadata for governance at scale, using MCP servers to teach models an organization’s language, and piloting agentic AI on low-risk workflows with measurable wins. Along the way, he highlights the upside of small language models, the primacy of data quality, and the simple truth at the heart of the 2025 Stack Overflow Developer Survey: AI is a force magnifier, but only when paired with human expertise and trustworthy knowledge systems.
Trust is slipping even as AI use rises. With 75% of developers seeking human validation, what’s driving that skepticism on your teams, and how has it shown up in code reviews or incidents? Can you share metrics or a story that changed your rollout strategy?
Skepticism is a feature, not a bug. Developers are paid to be precise, and they’re bumping into the same three friction points the survey surfaced: “almost right, but not quite” code, time-consuming debugging, and a lack of complex reasoning. We saw a pattern where AI-generated diffs looked syntactically clean and passed basic tests, but missed subtle invariants—pagination off-by-one logic, idempotency in retry handlers, and resource cleanup under timeouts. One memorable review, our senior engineer flagged a caching fix that worked on green paths but invalidated the wrong shard under a specific concurrency spike; it took an afternoon to reproduce and would have caused intermittent 500s in production. After a few of these near-misses, we formalized a rule: any AI-suggested change touching state, concurrency, or security requires human validation by a domain owner. That echoed what the survey found—75% still want a person involved—and it changed our rollout plan from “AI-first” to “AI-assisted with mandatory human signoff for risky areas.”
“Almost right but not quite” code is the top frustration. Where do those subtle errors usually hide in your stack, and how do juniors miss them? Walk us through a debugging session step-by-step and the safeguards you now require.
The gremlins hide where context is implicit: boundary conditions, integration seams, and performance-sensitive paths. In our stack, that’s pagination cursors, distributed locks, and schema migrations that rely on application-level ordering. Juniors often trust passing tests and miss invariants not encoded in tests—like “this method must be idempotent under retries” or “calls must be monotonic for rate-limited APIs.” A typical session starts with a vague report—say, occasional stale reads. We reproduce using a recorded traffic slice, add tracing to see cache keys and TTLs, and run chaos toggles to force evictions. The AI patch looked fine, but under concurrent writes, keys were derived before normalization, so two semantically identical records mapped to different keys. Safeguards now include: property-based tests for idempotency, contract tests for external APIs, and a “risk tags” checklist (stateful, concurrent, security, perf) that triggers required review and expanded test vectors before merge.
Advanced questions on Stack Overflow have doubled since 2023. What kinds of complex reasoning gaps are you seeing in production, and how do you triage them? Share an example where AI stalled and human expertise unlocked the solution.
The hard stuff blends systems thinking with messy real-world constraints: multi-system consistency, non-obvious backpressure, and operational edge cases. We triage by asking, “Is this a known pattern with clear guardrails, or does it require synthesis across systems?” AI helps enumerate possibilities but struggles to prioritize tradeoffs when the objective function is multi-dimensional—latency, cost, resilience, and safety. An example: we faced a cascading retry storm between two services with exponential backoff that synchronized under a shared time source. The model proposed backoff jitter and bulkhead isolation—both correct in theory—but missed the historical quirk that our job runner aligned batch windows at the same minute, causing burst alignment. A human ops lead recognized the pattern from a past incident and suggested a staggered schedule plus token-bucket admission. That human memory of “how our systems behave on Mondays at 9 a.m.” cracked it open where the model only offered textbook tactics.
Developers still turn to people when AI stumbles. How have you formalized human validation loops, and what quality signals (votes, accepted answers, tags) actually change outcomes? Give before-and-after metrics and a concrete workflow.
We built a lightweight, internal knowledge forum patterned after what developers already love. Every AI-generated suggestion that resolves a ticket gets posted as a candidate note with tags, links to traces, and a minimal reproducible example. Domain owners can vote, add comments, or mark “accepted,” and we’ll elevate those notes into playbooks only after expert verification. The quality signals that move the needle are simple: votes from recognized domain owners, accepted flags, and precise tags. Before we did this, “fixes” ping-ponged—engineers reopened 2–3 times on average. Afterward, we saw fewer reopenings because the accepted answer came with context: when to use, when not to, and caveats. The workflow is now muscle memory: propose, tag, discuss, verify, accept, and then sync into our RAG index.
You recommend spaces for knowledge curation and validation. What platform features mattered most, and how did you seed metadata and expert verification? Describe the launch plan, adoption curve, and one playbook that made it stick.
The must-haves were structured posts, tags, categories, labels, voting, accepted answers, and expert verification. We seeded metadata with a templated post type—Problem, Context, Attempt, Outcome, Next Steps—and auto-applied tags for service, language, runtime, and risk. For verification, we assigned expert pools per domain and rotated a weekly “on-call reviewer” to prevent backlog. Launch-wise, we started with a private beta for two squads, imported their incident retros and top Slack answers, and ran office hours. Adoption grew as engineers realized their notes weren’t disappearing into chat logs; the aha moment came when a new hire solved an outage in minutes by finding a tagged answer with a validated query. The playbook that stuck was “Write it once, read it 100 times”—every sprint retro had a five-minute slot to nominate one thread to codify and verify.
RAG is “having a moment,” with 36% learning it and search being the top AI use. What internal sources power your RAG, and how do you handle stale or conflicting docs? Share precision/recall metrics and an example query that saved real time.
Our RAG pulls from design docs, runbooks, incident reports, code comments, and curated Q&A. Because RAG is only as good as the data, we annotate every artifact with timestamps, owners, and lifecycle state. We resolve conflicts by ranking “accepted” and expert-verified content above raw notes; stale docs get a lower freshness score. We also surface dissenting views in the answer, with links, so engineers see tradeoffs. While I won’t invent numbers, the internal signal is clear: fewer context switches and faster first answers inside the IDE. A concrete win: a developer typed “blue/green deploy rollback for payments with stuck connections,” and the RAG summary pulled the exact drain commands, the health check grace window, and a note about a past incident when the load balancer needed manual connection draining. That saved a tense 30 minutes.
Data quality is the make-or-break. How do you audit for structure, freshness, and coverage, and who owns fixes? Give your scoring rubric, sample tags/categories that improved retrieval, and one incident where bad data misled an engineer.
We score every document across three axes: Structure (does it follow the template, include examples, and link to source?), Freshness (timestamp and last-reviewed window), and Coverage (does it answer the top user intents: setup, operate, debug, extend?). Ownership sits with domain stewards—each service has an owner who must keep the top playbooks and FAQs healthy. Tags that moved the needle were pragmatic: service name, lifecycle (beta/GA/deprecated), risk (security/stateful/performance), and environment (dev/stage/prod). One painful incident came from a stale runbook that suggested a feature flag ID that had been rotated; an engineer followed it and silently disabled a safety check in staging. The fix was twofold: demote stale docs in ranking and add a big banner when a doc exceeds its review SLA. Since then, similar mistakes have been far less likely.
Reasoning models like OpenAI’s o1 are still immature. How do you train for thought process, not just answers, using comments, decision logs, and debates? Show a concrete dataset schema and a training pipeline you’d recommend.
We shift from “final answer corpora” to “process corpora.” The best training data includes comment threads that show competing approaches, decision logs with alternatives considered, and postmortems that map hypothesis → experiment → outcome. A simple schema looks like: QuestionID, Context, CandidateAnswerA, CandidateAnswerB, CommentThread (timestamped), DecisionRationale, AcceptedAnswer, Tags, Owner, and OutcomeAfter30Days. For training, we build pairs and rankings: given the context and thread, which rationale led to the accepted answer? The pipeline: collect and normalize threads, redact secrets, generate weak labels from votes and acceptance, sample hard negatives (plausible but wrong), fine-tune with a step-by-step objective, and evaluate on held-out “advanced” questions. This rewards models that explain tradeoffs and cite constraints, not just spit out code.
Comments outrank even accepted answers for many users. How do you capture and rank comment threads so models learn tradeoffs and context? Share a specific thread that changed a decision, and the signals you used to weight it.
We capture comments as first-class citizens: each is a node with authorship, timestamp, and references to code snippets or logs. Ranking blends upvotes, author trust, recency, and diversity of perspectives—if three experts disagree, that’s a valuable signal that the space is nuanced. A thread that changed our minds was about schema evolution: migrate in place vs. dual-write with shadow reads. The accepted answer favored in-place for speed, but comments highlighted a historical race with backfilled data and recommended dual-write for one release cycle. We weighted that thread higher because the dissent came from domain owners and referenced a past incident report. That nuance became the template for similar changes.
Model drift is inevitable. What continuous feedback mechanisms catch it early, and how do you route fixes? Give a recent drift example, the metrics that flagged it, and the steps you took to retrain or roll back.
We monitor answer helpfulness via lightweight voting, compare models head-to-head on leaderboards, and watch regression tests crafted from real questions. When helpfulness dips or variance spikes, we treat it like a production incident: triage, bisect, rollback if needed. A recent drift appeared as overly confident answers on deprecated APIs; freshness signals were being underweighted. The leaderboard made it obvious—models that previously tied started diverging on “advanced” tags. We rolled back the retrieval re-ranker, rebalanced freshness in the scoring, and added a guardrail that injects deprecation banners directly into the prompt. Within a cycle, answers aligned again with verified content.
Tool sprawl persists, yet developers aren’t less satisfied. How do you decide when to add a tool versus extend one, and what integration patterns reduce context switching? Share your rules of thumb and a case where restraint paid off.
Our rule is simple: add a tool only if it provides a unique capability you can’t approximate with an existing one without undue friction. Otherwise, extend. Integration-wise, we bring answers to where developers live—IDEs, chat, and docs—so the cognitive tax stays low. We prefer clean APIs and SDKs, and we standardize auth flows so the experience feels coherent. Restraint paid off when we avoided adopting a separate runbook editor and instead extended our existing platform with templates and tags. Adoption soared because engineers didn’t need to learn yet another interface; more importantly, AI and RAG could index one canonical source of truth.
Agentic AI adoption is split: 52% avoid agents, yet 70% of adopters save time. What low-risk agent tasks worked first, and where did agents fail? Walk through a pilot plan, success metrics, and a postmortem from a miss.
We started with guardrailed tasks: log triage, test scaffolding, and doc linking. Agents pulled candidate runbooks for incidents, generated skeleton tests based on code diffs, and surfaced related design docs. They failed when we asked them to auto-remediate outside a sandbox; reasoning immaturity plus hidden system constraints made that too risky. The pilot plan mirrored the survey’s caution: choose low stakes, measure time saved, and require human confirmation gates. Success metrics included reduced time-to-first-answer and fewer context switches. A miss happened when an agent suggested a migration flag in the wrong environment—caught by the human gate, but it reinforced why the early use cases should be advisory, not autonomous.
You suggest piloting with interns or newer devs. Which onboarding tasks are ideal for agents, and how do you bound risk? Give a step-by-step playbook, including prompts, permissions, and the feedback loop you used.
Onboarding is perfect because mistakes are lower-consequence and the paths are well-trodden. Ideal tasks: environment setup, reading the top five architecture docs, running the app locally, and writing a first test. The playbook: 1) agent provides a guided checklist; 2) prompts are concrete (“Generate a step-by-step plan to run Service X locally, linking verified docs”); 3) permissions are read-mostly with write access only to the dev’s fork; 4) each step requires a human “confirm” to proceed; 5) feedback is captured as thumbs-up/down plus comments that feed back into the knowledge base. The loop turns onboarding questions into curated Q&A with tags and accepted answers. Interns become early signalers for confusing docs, which is gold for improving RAG.
MCP servers help models learn your org’s language and systems. How did you wire an MCP server into IDEs like Cursor, and what read/write rules mattered? Describe the bi-directional flow, a concrete task it enabled, and measurable gains.
We connected the MCP server as a data and action provider inside the IDE, so the model could fetch structured knowledge with quality signals and, with permission, write back summaries. Read rules allowed access to verified answers, tags, votes, and timestamps; write rules allowed saving a new “candidate note” only in sandboxes, pending expert verification. The bi-directional flow looked like this: developer asks a question, MCP fetches context with tags and accepted answers, the IDE shows a summary plus links; if the dev confirms a new insight, the model drafts a note with citations, which an expert reviews. A concrete task was “add observability to a new endpoint”—the IDE pulled the logging conventions, sampling rates, and past incidents that influenced our defaults. Gains showed up as time saved and fewer style nitpicks in review, because people followed the org’s language and patterns from the start.
Small language models are rising for domain tasks. Where did an SLM beat a general LLM in accuracy or cost, and how did you fine-tune it? Share training data sources, evaluation metrics, and one optimization that moved the needle.
An SLM tuned for our internal API patterns beat a general model on accuracy for code suggestions and cost by virtue of its size. We fine-tuned on our curated Q&A, code comments, and verified design snippets, emphasizing rationale and guardrails. Evaluation focused on functional correctness in harnesses, adherence to conventions, and ability to cite sources. The big optimization was prompt+retrieval shaping: we injected tags like service, lifecycle, and risk, and penalized answers without citations from accepted or verified docs. The result was fewer “hallucinated” endpoints and more consistent adherence to our defaults without paying for heavyweight inference.
APIs still make or break adoption. How do you evaluate API docs, SDKs, and pricing before committing, and what anti-patterns do you avoid? Give a recent integration example, including the endpoints, auth model, and time-to-first-success.
We look for clarity, examples, SDK availability, and transparent pricing. If there’s a mature SDK—like a TypeScript SDK that mirrors the API—we can judge integration speed quickly. Anti-patterns include opaque rate limits, inconsistent error codes, and auth flows that require bespoke gymnastics. A recent integration used a REST API with OAuth2, and the SDK let us hit “list resources,” “get details,” and “search” endpoints in minutes. Time-to-first-success was short because the SDK handled auth and provided typed responses; that is exactly how you reduce context switching and win developer trust.
Prosus classifies questions as “basic” or “advanced.” How would you mirror that classification internally to route issues and training data? Describe features you’d extract, a labeling process, and how you’d measure impact on resolution time.
We’d start with simple features: presence of multi-system context, concurrency, security, or performance tags; number of distinct subsystems referenced; and whether prior accepted answers exist. Questions with “advanced” signals route to domain owners and go into a special queue for deeper review, while “basic” ones get triaged by agents and peers first. Labeling is a mix of auto-tagging and human correction during review. We measure impact by tracking time-to-first-meaningful-answer and reopen rates across classes. Over time, the classifier also helps create better training splits—models learn to be humble on advanced territory and more assertive on basic patterns.
For RAG and agents, how do you enforce metadata hygiene at scale—tags, owners, timestamps, lifecycle? Share your governance model, automation you rely on, and a concrete cleanup that improved answer quality.
Governance is distributed but opinionated. Every artifact must have an owner, timestamp, lifecycle state, and tags. Automation helps: CI checks reject additions without required fields, nightly jobs flag stale docs, and a re-ranker demotes content past its review SLA. We run quarterly “taxonomy tune-ups” to merge duplicate tags and retire low-signal ones. A cleanup that paid off was consolidating five synonyms for “deployment strategy” into one canonical tag with subcategories—blue/green, canary, rolling. Answers got sharper because retrieval wasn’t diluted across near-duplicates, and agents started citing the best playbooks consistently.
When using third-party data, how do you test it against your quality bar and filter out noisy or outdated sources? Walk us through your evaluation checklist, a red flag you caught, and the remediation steps.
The checklist mirrors internal standards: source credibility, structure, timestamps, version alignment, and conflict with verified internal practices. We run sampling audits where humans rate a batch for usefulness and correctness, and we test retrieval with known queries to see if third-party content crowds out better internal answers. A red flag popped when an external doc suggested an unsupported feature flag format; it ranked high because of keyword overlap but contradicted our verified runbooks. Remediation was to downweight the domain, add a “conflicts-with-internal” tag, and require a freshness threshold for external sources on sensitive topics. After that, the internal, accepted answer correctly dominated.
If you had to pick one hardest-earned lesson, what would it be about balancing AI power with human oversight? Tell a story with the stakes, the metrics that mattered, and the exact changes you shipped afterward.
The hardest lesson is that speed without context is a mirage. We once celebrated an AI-assisted fix that closed a ticket in record time—only to learn later it introduced a subtle regression under peak load. The stakes were real: a customer-facing blip that eroded trust inside the team more than the outage itself. The metrics that mattered were reopen rates and mean time to clarity—how fast could we explain what happened? We shipped three changes: mandatory human validation on risk-tagged changes, process-oriented training data that captures tradeoffs, and freshness-aware retrieval so deprecated advice can’t sound authoritative. Since then, we’ve moved faster by insisting on guardrails, not despite them.
Do you have any advice for our readers?
Treat AI as an accelerant for human judgment, not a replacement. Invest first in your knowledge substrate—structured spaces, metadata, verification—because that’s what RAG, agents, and models will learn from. Start small with low-risk pilots, measure what matters, and build credibility through consistent wins. And remember what the survey showed: developers still lean on human expertise—more than 80% visit the communities they trust and 75% turn to another person when uncertain. Build that muscle internally, and your AI will get smarter every quarter.
