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Targeted FAQ Advanced Jp The Targeted FAQ template enables you to create personalized, audience-aware FAQ systems using retrieval-augmented generation (RAG), optimized prompting, and dynamic execution.
This advanced setup combines vector-based knowledge retrieval with prompt optimization and LLMC Executor for consistently high-quality, context-specific answers.

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Prerequisites

  • OpenAI API Key (required for both embeddings and model)
  • A structured FAQ or document file (PDF, TXT)
  • Basic familiarity with LLMC components

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Create the Targeted FAQ Flow

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1. Load Your FAQ Data

Start with the Load Data Flow (bottom section of the workspace).
This flow prepares your knowledge base for retrieval. Steps:
  1. Upload your document using the File component.
  2. Split the content into manageable text chunks via Split Text.
  3. Generate embeddings with OpenAI Embeddings (text-embedding-3-small).
  4. Store embeddings in LLM Vector DB for future semantic retrieval.
 Ensure your OpenAI API Key is added to the Embeddings component.

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2. Query FAQs for a Target Audience

Once your data is embedded, switch to the Retriever Flow (middle section). Components:
  • User Input – Enter persona or audience (e.g., “environment enthusiast”).
  • LLM Vector DB – Retrieves relevant FAQ chunks based on audience and query.
  • OpenAI Embeddings – Matches the same embedding model used in the loader flow.
  • Parser – Formats and cleans retrieved content for the LLM.
  • Prompt – Generates a question-aware, persona-specific FAQ response.
  • Chat Output – Displays the final answer directly in the playground.
Try modifying the persona field to see how answers adapt to different users.

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3. Optimize Your FAQ Prompts

Use the Prompt Optimizer Flow to refine your prompt instructions and improve FAQ response quality. How it works:
  • Automatically generates prompt variations and test cases.
  • Scores and ranks are prompted by relevance, accuracy, and tone consistency.
  • Displays a leaderboard of best-performing prompts.
To run it:
  1. Input your optimization task (e.g., “Generate the best FAQ tone for customer support”).
  2. Click Run on the Result node.
  3. Review and select the top-performing prompt from the leaderboard.

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4. Execute and Compare Results

Once optimized prompts are ready, use the LLMC Executor to run them. In the LLMC Executor:
  • Select your optimized Prompt and preferred Model (gpt-4o, claude-4-sonnet, etc.).
  • Paste your OpenAI API Key if not already configured.
  • Click Play to execute the query.
  • The Output displays your final, persona-tailored FAQ answer.

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Modify or Extend

  • Change the persona tone in the Prompt to match the brand voice or audience type.
  • Swap models in LLMC Executor to test performance differences.
  • Adjust chunk size and overlap in Split Text for better retrieval precision.
  • Use Results Leaderboard to continuously improve FAQ quality over time.

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Configuration Checklist

ComponentConfiguration
File / Split TextUpload and preprocess FAQ data
OpenAI EmbeddingsEmbedding model: text-embedding-3-small
LLM Vector DBStores embedded vectors for retrieval
Prompt Optimizer FlowGenerates and ranks prompt variants
LLMC ExecutorRuns the selected model and prompt
API KeyRequired for embeddings and GPT model access

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Example

Input: Target Audience – “College Students”
Output: FAQs rewritten in a simplified, relatable tone for students.

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Built With

  • LLMC Framework
  • Prompt Optimizer Flow
  • LLMC Executor
  • RAG Architecture (Vector Retrieval + Prompting)
  • OpenAI GPT Models
Deliver precise, audience-tailored FAQ responses with automation, retrieval intelligence, and optimized prompting. 

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