LLM Quality Monitoring — Sentry for AI Apps
Drop in three lines. Get alerted when your AI breaks before users do.
Build the LLM observability product that indie AI builders actually need. Flagship pitch: a drop-in quality and monitoring layer that catches regressions, hallucinations, and cost surges before users — and before your investor does. Real market: Helicone, LangSmith, Braintrust, Arize, and Patronus collectively raised $100M+ in 2025. Indie builders need a simpler, cheaper, opinionated version. Golden dataset, LLM-as-judge, live tracing, prompt A/B infrastructure, CI-gated regression protection, public scorecard. The portfolio outcome: a live dashboard URL hooked to your Project 1 and Project 2 services — the kind of artifact senior interviewers ask to see.
The product in detail
The product is the LLM-specific version of Sentry. Drop in three lines of code and get alerts when your AI feature regresses, hallucinates, or starts burning money. The user installs a lightweight SDK, sees a live dashboard within minutes, and wires alerting to Slack or email. Under the hood, the product runs continuous evaluations against a golden dataset, A/B-tests prompt variants on live traffic, and catches the exact moment a model upgrade or prompt tweak silently broke accuracy.
The target user is the indie AI builder — someone shipping an AI side project or running a small AI startup with 1–5 engineers. They've shipped a chat, agent, or RAG app. They know they should "have evals" but it always feels like next quarter's problem. The first time a user reports a hallucination in production, they realize how exposed they are. They'd pay $30/month for a simple observability layer that just works — no PhD required to set up.
Real market: Helicone (YC, $4M raised), LangSmith (LangChain's enterprise product), Braintrust ($36M Series A), Arize/Phoenix ($70M total), Patronus AI ($50M), Galileo. Collectively these companies raised over $100M in 2025–26 alone. Investors believe LLM observability is the new must-have category — like APM was for web apps in the 2010s. Indie builders are the underserved segment: they can't afford enterprise pricing and they don't have DevOps teams to integrate complex platforms.
Technically you build a golden dataset of 30+ curated test cases with adversarial edge cases, a Pytest harness running RAGAS metrics (Faithfulness, Context Precision, Context Recall, Answer Relevance), an LLM-as-judge scoring layer, a live tracing dashboard (latency p50/p95/p99, error rate, cost-per-day, model mix, hallucination-flag rate), a guardrails pipeline (input + output validation), and CI integration that blocks PRs when Faithfulness drops more than 5%. Cost optimization — the biggest tacit must-know in the research (Δ+8) — is built into the cost-attribution and semantic-caching features.
What you ship: a public scorecard URL displaying your AI feature's live metrics — this is the artifact every senior interviewer wants to see. A guardrails layer that blocks 10+ adversarial inputs in your demo. A CI gate that catches a planted prompt regression. The dashboard hooked to your Project 1 RAG and Project 2 agent with at least 7 days of real traffic data. When the interview is "how do you ensure quality in production AI systems," you show the dashboard, not slides.
Pick what you'll build
This is one project with one skill graph — but you pick the product framing that fits your interests or career goals. Each option below is a viable portfolio piece. We'll narrow it together in the kickoff call.
- Indie AI builder's monitoring layer — drop-in observability for any LLM app (audience: 10,000+ indie AI builders shipping side projects)
- Quality dashboard for your Project 1 RAG (your own AI product needs a quality moat)
- Quality dashboard for your Project 2 agent
- Vertical eval suite — pick a domain (legal / medical / finance) and build the eval taxonomy. Audience: ops/QA teams at AI startups in that vertical.
- Open-source eval harness for a category — chat, agents, or RAG. Audience: OSS-first engineering teams.
- Now that you have something to evaluate (P1 + P2), this project is the rigor pass.
- Evals are 46% of posts and 13% of interviews — strong in both.
- Cost Optimization is the biggest tacit must-know in the dataset (Δ+8).
- Guardrails as a discipline is +6 in interviews.
- All three get explicit checkpoint treatment here.
This project leans toward the Eval / Observability Engineer sub-track — dedicated roles exist at Arize AI, LangChain (LangSmith), Exa, and Notion (evals team).
Research backing this project
Every project on projeckt.ai is calibrated against real job posts and real interview questions, not opinion. Here's the data behind this one.
- •Cost Optimization (Δ+8, biggest tacit gap)
- •Guardrails (Δ+6)
- •Observability (balanced, both strong)
Themes covered
Each chip shows how many of the 46 analysed job posts cite this theme.
Justified by real job posts
Verbatim quotes from the postings that drove the design of this project.
“Shape the Future of AI Evaluation: Be at the forefront of designing new ways to measure and improve next-generation LLMs.”
“You'll use your deep technical expertise to help partners accelerate their impact through advising on evals, hill-climbing on harnesses, prototyping new agents”
“Optimize storage and query performance for high-volume observability and evaluation data.”
“Own scoped productionization projects: integrate new models/techniques, add monitoring and guardrails, or improve latency/cost/reliability”
What you'll be able to do
- 1Build a golden dataset (curated test cases including adversarial edge cases) for an LLM application
- 2Compute RAG-specific metrics: Faithfulness, Context Precision, Context Recall, Answer Relevance
- 3Implement LLM-as-judge scoring with multiple judge models and explain its limitations
- 4Compare two prompt versions on the same golden dataset with statistical significance (paired bootstrap)
- 5Instrument an LLM application with distributed tracing (every LLM call, tool call, retrieval call)
- 6Build a dashboard showing latency histogram, error rate, cost-per-day, model mix, hallucination-flag rate
- 7Design guardrails as a pipeline stage: input validation (PII, injection) and output validation (schema, moderation)
- 8Implement cost optimizations: prompt caching, semantic caching, model routing
- 9Triage a production hallucination: trace it, identify retrieval vs generation failure, propose fix
- 10Set up automated regression tests for prompts (CI runs eval on every prompt change)
Tools you'll learn
Hands-on with these tools across the paid and open-source stacks. The Paid / OSS / Both label shows which stack each tool belongs to.
Week-by-week checkpoints
Each checkpoint is a Pull Request to your public GitHub repo. Rohan reviews on the PR; merge = checkpoint complete.
30+ curated test cases (question, expected answer, expected sources, edge cases including unanswerable Qs). Pytest harness runs RAGAS Faithfulness + Context Precision + Context Recall + Answer Relevance. Scorecard output.
Dataset reviewed; harness runs in under 5 minutes; scorecard delta visible across 2 prompt versions.
- •Golden-dataset curation
- •RAG-specific metrics (Faithfulness, Context Precision/Recall, Answer Relevance)
- •Pytest as eval runner
LLM-as-judge scoring layer added. Compare 2 system-prompt variants on the same dataset. Output: which wins per metric with statistical significance (paired bootstrap).
Reproducible result — prompt B beats prompt A on metric X by Y%; report explains LLM-judge bias mitigation.
- •LLM-as-judge implementation
- •Multi-judge ensembling
- •A/B test design for non-deterministic outputs
LangSmith / LangFuse instrumented. Custom dashboard (Streamlit or Next.js) showing latency p50/p95/p99, error rate, cost/day, model mix, top-10 slow queries, hallucination-flag rate.
Live dashboard from real traffic to P1+P2; one cost optimization (semantic caching or model routing) implemented and impact shown.
- •Tracing instrumentation
- •Latency-bucket analysis
- •Semantic caching
- •Model routing
- •Cost attribution
Input guardrails (prompt injection detection, PII redaction) + output guardrails (schema validation, moderation, refusal detection) deployed inline. CI gate: every PR runs eval; merge blocked if Faithfulness drops >5%.
Adversarial test suite (10+ injections, 5 toxic outputs) — guardrails block all; legitimate traffic unaffected; CI failure blocks a planted regression PR.
- •Input / output validation pipelines
- •Prompt-injection defense
- •CI-gated prompt eval
Pick your stack
Same skills, different credentials. Start free with the OSS stack or use the paid stack to learn directly on production tools.
| Tool | Purpose | License | Hardware | Market equivalent |
|---|---|---|---|---|
| Pytest + RAGAS + DeepEval | Test + eval | Apache-2.0 | Free | Same (RAGAS is OSS-native) |
| Local LLM (Qwen 2.5 32B if HW allows, else 14B) as judge | LLM-as-judge | Various OSS | 16-32GB RAM minimum | GPT-4o judge |
| LangFuse self-hosted | Tracing + dashboards | MIT | Docker | LangSmith |
| NeMo Guardrails | Guardrails pipeline | Apache-2.0 | Free | Guardrails AI |
| Llama Guard 3 | Content moderation | Llama Community | Runs via Ollama | OpenAI Moderation |
Estimated cost: $0/month — fully local.
Interview questions you'll be ready for
Real questions from 13 sources, mapped to the skills this project builds.
Ready to start building?
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