back_office_ops · saas · workflow
Building Boba AI: lessons and patterns for LLM-powered co-pilot applications
Using an LLM effectively requires considerable prompt engineering skill from users, and LLMs cannot access current information beyond their training cutoff, limiting their usefulness for tasks requiring up-to-date knowledge.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · User enters strategic prompt
A user navigates to the Boba web application and enters a prompt for a product strategy task.
Tools used
GPT 3.5LangchainGoogle SERP APIExtract APIOpenAI APIStable DiffusionChatGPT
Outcome
The team built a working LLM-powered co-pilot that mediates between users and the LLM using templated prompts, structured JSON responses, context carrying, and embedded external knowledge, spending approximately 80% of development effort on the user interface.
Results
Time savedabout 80% on UI, about 20% on AI
Grounding & classification
Source type: technical build writeup
23 fields verified against source quotes.
agent assistcontent generationconversational airagsummarizationknowledge basehuman review describedmetric backedsource backedtools describedworkflow describedsoftwaretechnical build writeupback office opsagentic task executionrag answering