How Do We Work

Our Seven Step Process to Customize ChatGPT

Seven step process

Local ChatGPT Architecture

Local ChatGPT Architecture diagram

Process Defined

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1. Define Scope & Problem Statement

  • What does successful Secure ChatGPT look like?
  • How will we measure success? Increased productivity? Reduced cost? Increased sales?
  • Develop a mutual project plan
  • Identify a mutual resource plan
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2. Define Dataset & Documents

  • What documents are used for today’s business process?
  • How frequently are those documents updated?
  • Who needs access to those documents?
  • Where are those documents stored?
  • What is the most critical information in the documents?
  • Any PIA or HIPPA information?
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3. Survey User Pain Points & Queries

  • What information is frequently needed?
  • How long does it take to find information?
  • What are some of the nuances of documents?
  • How are documents generated?
  • What information can be wrong in the document?
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4. Modeling

  • Building the PrivateGPT
  • Ingesting the data
  • Prompt Engineering tuning
  • Knowledge-based tuning
  • Initial results accuracy & tone
  • Identify cases of hallucinations
  • Establish basic guardrails
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5. Piloting with Limited Users

  • Pilot the PrivateGPT with limited users
  • Gather user feedback
  • Identify false responses
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6. Iterate

  • Further tune the model parameters to increase accuracy
  • Reduce/eliminate hallucination
  • Ingest additional documents
  • Establish new accuracy
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7. Productionize PrivateGPT

  • Release the production version
  • Gather user feedback
  • Identify false responses
  • Monitor accuracy levels
  • Establish on-going support activities