The most common mistake people make with AI coding assistants is asking for the whole project at once: "Build me the entire thing." You get a pile of code that doesn't run, and when it breaks you have no idea where to look. The better approach is small steps with verification: write clear requirements, have the AI produce a plan, then build one small piece at a time and run each piece before moving on.

Here's the workflow I actually use, with integrating a SQL database into Azure AI Search as the example.

The workflow at a glance 

flowchart TD A[Write requirements doc] --> B[Upload doc to Grok] B --> C[Ask for a phased plan, no code yet] C --> D{Review the plan} D -- Needs changes --> C D -- Approved --> E[Ask for one small piece of code] E --> F[Run it] F --> G[Paste errors or results back] G --> H{All features checked off?} H -- No --> E H -- Yes --> I[Run acceptance questions: done]

Step 1: Prepare the requirements document

It doesn't need to be long, but these four sections must be clear.

1. Data Source

  • Which SQL database, and whether it's accessed through a connection string or a managed identity.
  • Which tables and fields get synced into the index.
  • Which fields stay out of the index and are only used to fetch full records back from the database.

2. Retrieval Requirements

  • Query type: keyword, semantic, vector, or a combination.
  • How many results each query returns.
  • Whether matching snippets should be highlighted.

3. Sync Strategy

  • Full sync or incremental sync.
  • How often it refreshes.
  • Who triggers it: a schedule, data changes, or a manual run.

4. Acceptance Criteria

  • Write down 3 to 5 real questions and the result you expect for each.
  • This is your pass standard: the feature is done only when all of them return the right results.

Step 2: Upload the doc and ask for a plan, not code

Upload the requirements document to Grok and say explicitly: "Don't write any code yet. Give me a phased implementation plan."

Review the plan yourself. Are the phases sensible? Is anything missing, like sync or permissions? Is the order right? Ask it to adjust until you're happy with the plan.

Step 3: Write the code one small piece at a time

Following the plan, ask Grok for just one small piece at a time, such as "create the index schema" or "set up the data source connection."

Run it right away. If it errors, paste the full error back and have it fix it. If it works, paste the result back too, then move on to the next piece.

Step 4: Loop until everything is checked off

Repeat Step 3, checking off each feature in the plan. Finally, run the acceptance questions from Step 1 one by one. You're done only when they all pass.

The key principle

Never let it generate the whole project at once. Small steps that can be verified and rolled back at any point are what make AI-assisted coding both fast and reliable.

The Azure AI Search + SQL architecture

  • Azure AI Search is the retrieval layer. It handles keyword, semantic, and vector search.
  • The SQL database is the storage layer. It holds the complete, authoritative data.
  • Azure Data Factory or Synapse syncs SQL data into the Search index.
  • At query time, search in Azure AI Search first, take the IDs from the results, then use those IDs to fetch the full records from SQL. 
flowchart LR SQL[(SQL database)] --> Sync[Azure Data Factory / Synapse sync] Sync --> Idx[Azure AI Search index] Emb[Built-in embedding skill] -->|generates vector fields| Idx Q[User query] --> Idx Idx -->|keyword + semantic + vector search| R[Result IDs] R -->|fetch full records by ID| SQL

You get a strong search experience while keeping a single source of truth in the database.

Recommendation: use text embeddings

Add text vector fields to your index. Azure AI Search has a built-in embedding skill that generates vector fields automatically while the index is created and data is imported, so you get vector and hybrid search without deploying or running a model yourself.

Write clear requirements, get a plan first, code in small steps, and verify each one. Follow that workflow and your AI coding assistant will actually help you finish the project, instead of handing you a pile of code that won't run.