What is an AI First MVP?
An ai first mvp puts AI features at the center from day one. That means the product’s main value comes from answering questions, finding documents, or extracting actions using a language model that is grounded by external data.
Why not fine tune a big model? Fine tuning is slow and costly. Instead, use retrieval augmented generation, store your facts in a vector database, fetch the most relevant text for each question, then ask an LLM via a plugin to answer using only that text. The result is faster to build, cheaper, and much less likely to hallucinate.
This approach works well for quick Q&A, knowledge bases, and internal help desks. It is a poor fit when the task is deeply creative or needs broad world knowledge that isn’t in your documents.
Why Use LLM Plugins & Vector Search in Your MVP?
Vector search finds text by meaning rather than exact keywords. You turn documents into vectors, a query becomes a vector, and the database returns nearby vectors, which are the pieces of text most likely to answer the question.
LLM plugins let the model call external tools or your database. That helps stop the LLM from inventing facts: feed it retrieved text and tell it to answer only from that text and to cite sources.
A common mistake is relying on keyword search alone. Semantic matches catch paraphrases and synonyms. The trade off is extra engineering up front for embeddings and a vector database, but you avoid bad answers later.
Practical check: confirm your documents are mostly natural language, like articles or FAQs. If you have tables, code, or highly structured reports, plan special chunking or converters.
The 30 Day AI First MVP Development Roadmap
This is a hands on weekly plan that worked for teams I know. Adjust timing based on your team size.
Week 1 : Get the data in shape and index it
Pick a small set of documents, roughly 50 to 500, that matter for your initial use case. Chunk the text with overlap so meaning isn’t split; start with 25 token chunks and about 40 percent overlap for narrative text to keep sentences intact across boundaries. Choose an embedding model, bge small for speed and lower cost or bge large for better accuracy if you can afford more compute. Deploy a vector database such as Qdrant, cloud or self hosted, and ingest the chunks.
Check whether your docs are private or regulated. If so, plan for encryption and on prem hosting early. If this step fails, it’s usually because very long technical reports break fixed size chunking; use structural or semantic chunking instead.
Week 2 : Build the retrieval API
Create an API that embeds a user query and runs a hybrid search, combining vector similarity with metadata filters. Add filters so you can restrict results by date, author, or document type. Start with a k limit of 5, returning five records, then test raising it while watching how many chunks the answer LLM can handle.
What takes more time than people expect is building reliable metadata and search logic. Don’t skip tests with edge queries and unusual metadata combinations.
Week 3 : Wire up the LLM plugin and prompt rules
Integrate an answer LLM via its plugin or API. Options that teams use include Gemini 2.0 flash for high quality, Gemini 2.0 flash lite for lower cost, Mistral saba 24b, or Gemma2 9b it for on prem or open models. Construct a prompt that supplies only the retrieved chunks and explicitly tells the model: answer only from the text below; if the answer is not present, say “I don’t know” and cite the source. Keep temperature low, around 0 to 0.3, to reduce invention.
A frequent mistake is letting the LLM freeform. When you need factual answers, constrain the output and the evidence the model can see.
Week 4 : Tune, evaluate, and prepare to ship
Tune chunk size, embedding model, and k limit. For short biographies we saw 25 token chunks perform best; for manuals or legal documents use larger, structure aware chunks. Run a test set of questions, 50 to 300, and use an evaluation LLM or human checks to measure accuracy and hallucinations. Monitor cost closely since token counts and model selection drive spending; try flash lite equivalents where possible.
You will need to choose between accuracy and cost. A larger k limit and a bigger LLM improve answers but increase expense.
Key Technical Components of the AI First MVP
Data chunking
Balanced chunk size keeps context readable and small enough to fit the model’s context window. Overlap prevents chopping key facts. This works well for narrative text, articles, and FAQs, but fails on heavily structured tables. For tables, use parsers or treat rows as separate chunks.
Embedding models
Smaller embedding models are faster and cheaper while larger models give better semantic matches. If you reindex often, prefer a smaller model to save compute; if you index once and want top accuracy, pick the larger variant.
Vector database and hybrid search
Qdrant is simple and fast for production. Hybrid search, combining vectors with metadata, helps when exact terms matter, such as codes or names. If you forget useful metadata, similar but wrong items can surface.
LLM context window and k limit
The LLM can only read so many tokens. Calculate how many chunks fit before the prompt exceeds the model’s window and keep the k limit low for small context models. For huge context models, cost often becomes the limiter rather than the window.
Cost considerations
Token costs vary by model and provider. Estimate monthly tokens from average context plus answer length and expected query volume. Use cheaper lite variants for bulk usage when accuracy requirements allow.
Avoiding Common Pitfalls and Limitations
Hallucinations usually happen when the model is allowed to invent beyond the evidence. Only provide retrieved text and instruct the model to cite or say “I don’t know” if the evidence is missing. Fixed chunk sizes break on long structured docs; use semantic or structure based chunking. Short, one line answers save money but may be insufficient for users who need detail. Tests run in a small lab often understate production latency and cost, so plan monitoring and load tests early.
A mistake I see often is assuming retrieval alone guarantees correctness. Rerank and evaluate results before sending them to the LLM.
Tools and Resources to Accelerate Your AI First MVP
Here are the common tooling choices teams start with. They are quick to set up and cover most needs for an MVP.
- Vector DBs: Qdrant, Pinecone, Weaviate, Milvus, with Qdrant a solid start for cloud or self hosted deployments
- Embeddings: bge small, bge base, bge large variants; pick based on needed accuracy versus compute
- LLMs: Gemini 2.0 flash, Gemini 2.0 flash lite, Mistral saba 24b, Gemma2 9b it for on prem or open options
- Evaluation: a separate LLM or human review set to catch hallucinations and ranking errors
- Speed: consider GPU accelerated vector indexing, for example NVIDIA cuVS, if you index many vectors or need low latency
Pick the simplest stack that fits your constraints. If you expect frequent reindexing, optimize for cheaper embeddings and faster pipelines. If you expect low update frequency but higher accuracy needs, prioritize the larger embedding and answer models.
Beyond the MVP — Future Directions
After the MVP, test adaptive chunking, better token control for longer answers, and domain specific embeddings for medicine or law. Add automatic hyperparameter tuning for k limit and reranking if traffic and data grow. Also plan for privacy: encrypt data and consider on prem solutions when handling sensitive information.
Conclusion
This approach gets you a working ai first mvp quickly: index the documents that matter, run semantic search, and let the LLM answer only from retrieved text. You avoid many hallucination problems and can keep costs reasonable by matching chunk sizes, embeddings, and models to your needs.
Next step: pick a small, meaningful dataset, for example one product manual, a set of FAQs, or 100 internal documents. Spend one week on ingestion, then build a simple API that returns the top five chunks for a query. Test answers with real questions, watch costs and response quality, then decide whether to swap embedding models or the answer LLM. That loop - index, retrieve, generate, evaluate - is where you learn the real trade offs and where small tweaks produce the biggest improvements.
