Turn Your VS Code Into A Generative AI Prompt IDE for RAG Applications

Generative AI teams can turn VS Code into a “prompt IDE” for RAG by using AI config: prompts, model choices, and runtime settings live in version-controlled JSON/YAML files, then run and evaluated through an SDK. The practical payoff is faster iteration—prompt changes can be tested inside the same development environment where application logic is built—plus a more reliable path to production because the exact model and prompt configuration behind deployed behavior can be traced over time.

Last Mile AI frames the problem as a full-stack bottleneck: many engineers building generative applications aren’t ML specialists, yet they still need dependable prompt and model management. AI config addresses that by separating “business logic” code from “generative AI signature” artifacts. In practice, the VS Code extension renders any AI config.yml or AI config.js file as a notebook-style editor. That editor supports prompt templates (including handlebar-style parameter injection), model selection across providers, and multimodal workflows—so prompt exploration happens alongside application development rather than in a separate playground.

The walkthrough builds a simple RAG question-answering app over a large text source (a “volume 7” history-of-the-United-States file). A Python notebook ingests the text by chunking it into fixed-size 1,000-character segments and stores embeddings in a Chroma vector database. Queries then retrieve relevant context and feed it into a generation prompt running GPT 3.5 turbo. Early results illustrate why evaluation matters: a baseline prompt produced an incorrect claim about the price of flower in Boston (July vs. August), showing that retrieval/context alignment and prompt behavior can fail even when the pipeline runs.

Evaluation is handled with model-graded eval using GPT-4. The system measures four criteria: relevance (answers match the question), coherence (readability), faithfulness (answers adhere to retrieved context), and succinctness/structure (responses meet formatting and brevity expectations). Evaluation prompts accept the query, the generation output, and—where needed—the retrieved context. The workflow also highlights a key limitation: GPT-4-based grading is expensive and not perfectly reliable, so prompt engineering for the evaluator still requires iteration.

To move beyond one-off “ad hoc” checks, the approach emphasizes running the same query multiple times (trials) because LLM outputs are nondeterministic unless seeds are controlled. With repeated runs across many queries, teams can compute statistically meaningful distributions of scores rather than trusting a single sample. The next step—still work in progress—is closing the loop from metrics to optimization: specifying an optimization target (e.g., improve faithfulness) and automatically sweeping prompt/model parameters, similar to hyperparameter tuning.

AI config also supports multimodal prompt chains. Examples include using Hugging Face tasks for image-to-text and then feeding generated captions into downstream LLM prompts for translation, or chaining local inference with remote models. Under the hood, attachments and modality-specific outputs are abstracted so developers don’t need to manually handle base64 images, audio formats, or provider-specific invocation logic.

Overall, AI config positions evaluation and prompt management as the missing “debugger-like” layer for RAG pipelines—one that can reduce hallucinations and cost by making quality measurable, repeatable, and versioned, while enabling rapid experimentation in the same IDE used to ship the application.