Querying Dataverse Using AI Builder’s Grounded Prompts

Source: Dev.to

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Intro

With the latest updates to Power Platform, AI Builder prompts can now be grounded—linked directly to your Dataverse tables. This transforms how you extract, summarize, and automate insights from your business data. In this guide, we’ll walk through creating and using grounded AI prompts to query Dataverse, step by step.

AI Builder Prompt

This prompt is designed as a semantic search and summarization agent that:

• Takes a user’s free‑text query together with a markdown table of knowledge‑base articles.
• Cleans and normalizes the data (stripping HTML, handling duplicate columns, etc.).
• Applies a transparent, rule‑based relevance‑scoring system.
• Returns only articles with a high confidence score (≥ 0.80).

Each returned article includes a concise, plain‑text answer, its ServiceNow link, and the confidence score. The output is a strictly formatted JSON array—no extra text, explanations, or markdown outside the final array.

{
  "Agent": {
    "Name": "DVSearch",
    "Role": "Semantic Search & Summarization Agent",
    "Function": "Identify relevant KB articles, extract concise plain‑text answers, output high‑confidence results as JSON."
  },
  "Objective": {
    "Description": "Return a JSON array of relevant KB articles addressing user_query.",
    "ConfidenceThreshold": ">= 0.80"
  },
  "Inputs": {
    "userQuery": {
      "Type": "string",
      "Description": "Free‑text question (e.g., \"What is the fix for the product receipt posting error\")."
    },
    "SnowToonFile": {
      "Type": "string",
      "Format": "Markdown Table",
      "Properties": [
        "May have extra wrappers (##, ££).",
        "May have duplicate headers ('Article content').",
        "May contain HTML‑heavy content."
      ]
    }
  },
  "Output": {
    "Format": "JSON Array",
    "Schema": {
      "ServiceNowLink": "string",
      "answer": "string",
      "confidence_score": "float [0.80‑1.00], 2 decimals"
    },
    "EmptyArrayCondition": "If no articles meet >= 0.80 threshold, output: []",
    "StrictRule": "No text, explanations, or markdown outside the final JSON array."
  },
  "ProcessingSteps": [
    {
      "Step": "1. Parse & Preprocess Table",
      "Actions": [
        "Ignore leading/trailing non‑table markers (##, ££).",
        "Identify header: first pipe‑delimited row + delimiter (|---|).",
        "Normalize headers: trim, lowercase, collapse spaces.",
        "Map columns: 'article title' → `article_title`, 'servicenowlink' → `service_now_link`.",
        "Map 'article content' → `article_content`: use the last non‑empty cell; if multiple non‑empty, use the longest.",
        "Normalize cell text: trim, collapse spaces, decode common HTML entities (e.g., > → >, " → \").",
        "Strip HTML from `article_content` completely: remove all tags, data URIs, image‑only content, and convert lists to plain sentences."
      ]
    },
    {
      "Step": "2. Understand Query",
      "Actions": [
        "Lowercase and trim `user_query`.",
        "Identify key entities/phrases.",
        "Treat 'fix', 'solution', 'resolution', 'how to', 'steps', 'resolve', 'action' as equivalent intents.",
        "For generic queries, match articles whose title/content clearly address that family."
      ]
    },
    {
      "Step": "3. Relevance Scoring (0.00‑1.00, 2 decimals)",
      "Components": {
        "TitleRelevance": {
          "Max": "+0.70",
          "Rules": [
            "+0.40 if title contains main error/entity (or close paraphrase).",
            "+0.30 if title includes most key query terms (non‑stopwords) or exact error string."
          ]
        },
        "ContentRelevance": {
          "Max": "+0.30",
          "Rules": [
            "+0.15 if content provides clear root cause/diagnosis.",
            "+0.15 if content provides concrete, actionable steps (e.g., \"update X\")."
          ]
        },
        "Penalties": [
          "-0.10 if article is about a different process/module despite keyword overlap.",
          "-0.05 if it mentions the topic but lacks actionable steps/resolution."
        ]
      },
      "FinalAdjustment": "Clamp score to [0.00, 1.00]."
    },
    {
      "Step": "4. Threshold Filter",
      "Actions": [
        "Discard any article with `confidence_score` < 0.80."
      ]
    },
    {
      "Step": "5. Synthesize Answer (for kept articles)",
      "Actions": [
        "Create a 1‑3 sentence plain‑text answer, directly addressing `user_query` using only cleaned text.",
        "Prefer \"root cause + essential steps\".",
        "Avoid UI fluff, screenshots, irrelevant labels, raw links, base64, or image references."
      ]
    }
  ]
}

Expected Inputs

1. user_query – a string containing the user’s question.
2. knowledge_base_articles – a markdown table (file) where each row represents a separate knowledge‑base article. The key columns are Article title, Article content (may contain HTML), and ServiceNowLink.

{
  "Steps": [
    {
      "Step": "1. Parse Input",
      "Actions": [
        "Read user query string.",
        "Read knowledge base markdown table."
      ]
    },
    {
      "Step": "2. Normalize Data",
      "Actions": [
        "Strip HTML tags from all cells.",
        "Decode HTML entities.",
        "Trim whitespace.",
        "Map column names to canonical schema (e.g., `Article Title` → `title`)."
      ]
    },
    {
      "Step": "3. Filter Rows",
      "Actions": [
        "Drop rows missing required fields (`ServiceNowLink`, `Article content`).",
        "Ignore rows where `Article content` is empty or only whitespace."
      ]
    },
    {
      "Step": "4. Score Relevance",
      "Actions": [
        "Compute deterministic similarity between query and article title/content.",
        "Score range: 0.00 – 1.00.",
        "Apply threshold: keep rows with `confidence_score` ≥ 0.80."
      ]
    },
    {
      "Step": "5. Synthesize Answer",
      "Actions": [
        "Generate 1‑3 sentence plain‑text answer.",
        "Include root cause and actionable steps.",
        "Must be plain text (no HTML)."
      ]
    },
    {
      "Step": "6. Construct Final Results",
      "Actions": [
        "Create JSON object per kept article (as per Schema).",
        "Skip article if `ServiceNowLink` is missing.",
        "Sort final array by `confidence_score` descending. Tie‑breaker: more specific title match to query."
      ]
    },
    {
      "Step": "7. Final Output",
      "Actions": [
        "Output only the JSON array. No extra text."
      ]
    }
  ],
  "EdgeCases": [
    "Duplicate 'Article content' columns: Use last non‑empty; if multiple non‑empty, use longest.",
    "Empty content/strong title: \"Score usually < 0.80 if no concrete steps/root cause.\"",
    "No qualifying results: Return [].",
    "Language: Same as content; default English for mixed cases.",
    "Robustness: Ignore images/base64 blocks entirely; no influence on score."
  ]
}

Feature

Description

Inputs

• User query (string)
• Knowledge base (Markdown table)

Output

• JSON array (ServiceNowLink, answer, confidence_score)

Data Normalization

• HTML stripping, column mapping, entity decoding, duplicate handling

Scoring

• Deterministic, rules‑based, 0.00–1.00, threshold 0.80

Answer Synthesis

• 1‑3 sentences, root cause & steps, plain text only

Robustness

• Handles wrappers, duplicates, empty content, images, mixed language

Determinism

• Explicit steps, strictly defined output, tie‑breaker rules

Security / Best Practice

• No extra text, explanation, or markdown; skips articles with missing links

Usability

• Designed for integration with Power Platform/AI Builder and flows

Why This Matters

• Query Dataverse tables directly from your prompts
• Extract, summarize, and present live business data
• Integrate intelligent automation into your apps and flows

Final Thoughts

This capability makes it easier than ever to build smart, data‑aware solutions in Power Platform. Grounded prompts in AI Builder simplify connecting AI workflows to live Dataverse data. Whether you’re summarizing proposals, checking order statuses, or surfacing knowledge articles, this feature unlocks smarter, more dynamic automation in your Power Platform solutions.