Writer
Ravi Menon
Principal AI Engineer
Previously ML platform engineering at scale
Ravi builds the applied AI systems we ship: retrieval pipelines, evaluation harnesses and the guardrails that keep a model useful once real users reach it. He writes about the engineering that decides whether an AI feature survives production.
- LLM evaluation
- Retrieval-augmented generation
- Prompt engineering
- AI cost modelling
- 4
- Articles
- 4
- Expertise
- AI Engineering
RAG that actually retrieves: fix the retrieval layer first
Most retrieval-augmented generation problems are retrieval problems wearing a generation costume. Fix chunking, hybrid search and reranking before you touch the prompt.
RMRavi Menon2 min read - AI Engineering
Evaluating LLM features before you ship them
Most AI features fail in production because nobody built a way to tell whether a prompt change made things better or worse. Here is the evaluation harness we build first.
RMRavi Menon2 min read - AI Engineering
What an AI agent actually costs per completed task
Token pricing is not the interesting number. Cost per successfully completed task, including retries and human escalation, is the one that decides whether an agent ships.
RMRavi Menon2 min read - AI Engineering
Chatbot guardrails that hold up in front of customers
Refusals, scope limits and escalation are product decisions, not prompt lines. Here is the guardrail stack we ship on customer-facing assistants.
RMRavi Menon1 min read
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Guides
- Case study: 240 creator posts in one quarter without losing brand safety
Eighty-four creators, four markets, one brief. The operational decisions that kept quality consistent at that volume.
- Case study: replacing a nine-year-old CRM in fourteen weeks
Forty-one undocumented behaviours, 1.8 million records and a sales team that could not stop selling for a weekend. Here is how the sequencing worked.
- Short-form video: what is actually trending, and what just looks new
Most trend reports describe last quarter. These are the shifts that changed campaign economics rather than aesthetics.
- Case study: cutting retail LCP to 1.2s and lifting mobile conversion 11%
A mid-market retailer with a 3.4 second mobile LCP. Six weeks, no redesign, no re-platform — and a conversion result the finance team could sign off.