Practice · 2023–now · AI Present
Chatbot wrapper as product
Slap an LLM on the homepage and call it AI-native — "we have an app now," 2010 edition.
Why Chatbot wrapper as product became a costly fad
Chatbot wrappers failed as products because a prompt over public docs is a feature, not a company. They stuck as support widgets with clear metrics. The costly fad was fundraising on the chat box while competitors shipped workflows with ownership and evals.
Cost of the fad
What Chatbot wrapper as product cost
Homepage chat boxes billed as AI-native platforms while the roadmap stayed FAQ search with a spinner. Acquisition decks outpaced retention.
Patterns
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LLM app frameworks
LangChain-class glue mutated into MCP clients and thin wrappers around vendor APIs. The durable pieces are still boring: evals, retrieval, and product UX.
Practice · 2023–now
RAG as default architecture
Retrieval-augmented generation as the answer to every knowledge problem — then "just stuff the window" as the counter-fad. Often right; often Postgres with pgvector would suffice.
$ Teams stood up vector pipelines, chunking strategies, and rerankers before asking if fine-tuning or a SQL query would answer the question. Retrieval infra became the product.
Practice · 2024–now
Eval theater
LLM evals as slideware — metrics that look scientific and never gate a deploy. The foil to real eval pipelines in CI.
$ Dashboards of vibe-check scores that never blocked a release. Green charts for leadership; prod still hallucinated.
Practice · 2023–now
Prompt injection as afterthought
Ship the chatbot, bolt on security later — OWASP's new category, same old afterthought.
$ Customer-facing chatbots shipped with tool access and "security in phase two." Indirect injection via docs and tickets became the new XSS.