back_office_ops · education · workflow
How Reforge Built Their AI-Powered Browser Extension Using RAG and Chain of Thought
Reforge users consistently struggled to apply course content to their actual daily work, even after Reforge launched Artifacts to bridge the gap.
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 requests document help
When the user clicks 'Help Me Improve My Document', the contents of their document are retrieved with JavaScript.
Tools used
PineconeLaunchDarklyAdalineSegmentMetaBaseAmplitudeSnowflakeRetool
Outcome
After upgrading from simple RAG to a Chain of Thought approach with explicit document classification, the distribution of course recommendations diversified and less than 50% of responses contained no Reforge references, with further increases in references seen after upgrading to version 4.0.
What failed first
The initial simple RAG setup did not produce reliable outputs, and the LLM failed to accurately classify document types, resulting in irrelevant suggestions being shown to beta testers.
Results
Volumeless than 50%
Grounding & classification
Source type: technical build writeup
27 fields verified against source quotes, 1 dropped as unverifiable.
content generationdocument classificationknowledge searchragknowledge basebuilder submittedfailure mode describedmetric backednamed customertools describedworkflow describededucationsoftwareemployee productivitytechnical build writeupback office opsrag answering