Automating Reseller Content Generation Workflow

Purpose:

To automate the generation of content by mapping dynamic variables in a structured format using a dataset. The goal is to integrate this process into an AI-powered application to scale and customize content for various resellers. This is demonstrated with "Softchoice" as an example, but the workflow is designed to be scalable across different resellers and solutions.

Overview:

  1. Initial Dataset Creation and Mapping:
    You have a dataset (CSV file) that includes specific information about resellers (e.g., Softchoice) and HYCU. This dataset serves as the primary source for dynamic variables, which are mapped to placeholders in a Canva or Google Slides template. The variables act as "merge fields" that the automated bulk creation process uses to create tailored presentations or documents.

  2. Context Expansion for Dynamic Variables:
    You aim to automate the enrichment of context, instructions, tasks, structured outputs, prompts, exact answers, and summaries for each variable. These are organized into the following columns in a structured format:

    • Value
    • Context
    • Instructions
    • Task
    • Structured Output
    • Prompt
    • Exact Answer
    • Summary

  3. Automating Content Creation using Canva/Google Slides/Sheets:
    Data from the structured CSV is mapped to dynamic fields in Canva or Google Slides, which generate personalized and contextually relevant content. You are considering multi-select options and varying text inputs for these fields.

Steps and Sub-Steps:

1. Prepare the Initial Dataset:

  • File: Softchoice Cheat Sheet Fields (1).csv (or similar for other resellers)
  • Columns: Map each column to corresponding values. For example, "Joint Value Answer," "Elevator Pitch," "Competitive Message Templates," etc.
  • Field Mapping: Ensure that columns are labeled as generic placeholders (like {VAR}) so the dataset can easily be expanded to other resellers.

2. Generate the Enhanced Dataset with Dynamic Variables:

  • Tools Needed: Google Sheets (for structured data) and Canva or Google Slides (for templated design).
  • Headers: | Value | Context | Instructions | Task | Structured Output | Prompt | Exact Answer | Summary |
  • Questions for Each Header: Create LLM-generated questions to expand each header into a detailed context, task, or instruction set. For instance:
    • Value: “Describe the core value of {VAR}’s joint offering with HYCU.”
    • Context: “What challenges does {VAR} face in data protection?”
    • Task: “What are the essential steps in {VAR}’s data protection strategy with HYCU?”

3. Automate the Bulk Creation Process in Canva/Google Slides:

  • Dynamic Field Mapping: Link the enhanced dataset columns to placeholders in the Canva or Google Slides template (TEMPLATE_CHEAT SHEET24). This involves setting up merge fields like {JOINT_VALUE}, {RISKS_DATA_LOSS}, {PERSONA_1}, {DISCOVERY_1}, etc.
  • Multi-Select Options: Specify multi-select fields in the spreadsheet to allow dynamic selections like “Target Industry” and “Customer Segment.”
  • Bulk Upload Automation: Use Canva’s or Google Slides’ bulk creation functionality to automatically generate tailored documents based on the mapped data.

4. Generate Questions for Content Enrichment:

  • Optimize the Question Set: Design a set of questions that will be run through the LLM to generate answers corresponding to each header.
  • Store and Iterate Questions: Ensure that questions can be iterated over to create a variety of enriched content for each dynamic field.

5. Integrate with LLM for Contextual Enrichment:

  • Application Workflow (as detailed in the document V2 GPT.pdf):
    • Input Parameters: Form fields that trigger specific LLM workflows based on variables like “Target Industry,” “Customer Segment,” etc.
    • Information Retrieval: Dynamically retrieve context, technical details, and value propositions based on input fields, combining hybrid information retrieval with AI-generated responses.

6. Refine the Templates Based on Feedback:

  • Template (CHEAT SHEET TEMPLATE (BULK EDITOR)): Validate the integration with the dataset by running bulk edits.
  • Review and Update (Completed Cheat Sheet): After running through the bulk create, compare the results with intended output and refine as needed.

7. Automate JSON/CSV File Generation and LLM Integration:

  • Tag and Parse: Automatically parse the generated content back to the original knowledge base and tag for future retrieval.
  • Store Data in JSONL Format: Convert the enhanced dataset into JSONL format for scalability and integration into the larger knowledge base.

Recommended Tools and Resources:

  • Tools: Canva, Google Sheets, Google Slides, Replit, various APIs (OpenAI, Anthropic, Meta, Cohere, etc.), Google Cloud services, PDF.co for parsing PDFs.
  • Libraries: Pandas for data handling, Beautiful Soup/Selenium for web scraping if required, and LangChain for task-specific orchestration.
  • Design Resources: Canva/Google Slides for templates, Miro/Beautiful.ai for conceptual design.

Future Considerations:

  • Build a dataset specifically for the knowledge base integration and align it with ongoing hybrid information retrieval and content generation tasks.
  • Expand the application workflow for ISVs and resellers by integrating with external databases, creating more intricate automation rules, and implementing dynamic form generation.

This README file encapsulates the workflow, tools, steps, and considerations needed to automate and scale your content generation project. """

Save the content to a file

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