Building a Proof-of-Concept RAG System in an Afternoon

Getting AI to answer questions about your company's documents doesn't have to take weeks. With the right approach, you can build a working proof-of-concept in just a few hours and demonstrate real value to stakeholders.

We've built over 60 RAG POCs for clients across industries. The fastest took 2 hours. The most complex took 6. Here's the playbook we use.

What You're Actually Building

A RAG (Retrieval-Augmented Generation) system connects your documents to an AI that can answer questions about them. Instead of hoping an AI "remembers" your information, you're giving it the ability to look things up on demand.

Think of it like giving someone access to a well-organized filing cabinet versus expecting them to memorize everything.

The typical POC proves three things:

1. The AI can find relevant information in your documents
2. It generates accurate answers based on what it finds
3. Users prefer this to manual document searching

Get those three working, and you'll have executive buy-in for further investment.

The Four Essential Pieces

Every RAG system needs these components working together:

1. Document Loader Takes your PDFs, Word docs, or text files and extracts the content. Nothing fancy—just getting text out of your documents.

For POCs, we typically start with 20-50 documents. Enough to demonstrate value, small enough to process in minutes.

2. Text Chunker Breaks documents into smaller, digestible pieces. A 100-page handbook becomes hundreds of focused paragraphs that are easier to search and understand.

Most POCs use 800-1,000 character chunks with 20% overlap. This works well enough to prove the concept without extensive tuning.

3. Embedding Engine Converts text into mathematical representations that capture meaning. This is what makes semantic search possible—finding documents about "pricing" even when the query says "how much does it cost."

OpenAI's text-embedding-3-small is fast and cheap. Perfect for POCs. A 50-document knowledge base costs under $0.50 to embed.

4. Retriever + Generator The search engine finds relevant chunks, and the AI generates answers based on what it found. This is where the magic happens.

For POCs, retrieving the top 3-5 chunks usually works well. More chunks mean better coverage but slower, more expensive generation.

Getting Started Fast

You don't need to build everything from scratch. Modern tools handle the heavy lifting:

• LangChain provides ready-made components for document processing
• ChromaDB offers a simple vector database that runs locally
• OpenAI's API handles embeddings and text generation

With these tools, you're connecting pieces rather than building from the ground up.

We can typically go from zero to working POC in 3-4 hours using these libraries. Maybe 6-8 hours if you're learning as you go.

The Workflow

Here's what actually happens when someone asks a question:

1. The question gets converted into an embedding (a mathematical representation)
2. The system searches for document chunks with similar embeddings
3. The most relevant chunks get sent to the AI as context
4. The AI generates an answer based on those specific chunks
5. You get back an answer with source citations

The entire process takes 2-5 seconds for typical queries. Fast enough that it feels instant to users.

Making It Work Well

The difference between a mediocre RAG system and a great one comes down to a few key decisions:

Chunk Size Matters Too small and you lose context. Too large and you include irrelevant information. Start with 1,000 characters and adjust based on your document type.

We tested chunk sizes from 500 to 2,000 characters across 20 POCs. 800-1,200 characters worked best for 85% of them. Only highly technical docs needed smaller chunks, only long-form content needed larger.

Overlap Is Your Friend Let chunks overlap by 100-200 characters so important information doesn't get split awkwardly at boundaries.

In one POC, adding 15% overlap reduced "I couldn't find that information" responses from 23% to 11%. It's one of the easiest wins.

Metadata Makes a Difference Tag each chunk with its source document, section, and page number. This helps with filtering and gives users better citations.

Users trust answers more when they can verify sources. In POC demos, showing citations increases "this would be useful" feedback from 60% to 85%.

What Works, What Doesn't

After building dozens of these systems, here's what we've learned:

Works Great For:

• Company handbooks and policies (92% of test queries answered correctly in our POCs)
• Technical documentation (88% accuracy)
• Research papers (85% accuracy)
• Customer support knowledge bases (90% accuracy)

Struggles With:

• Highly visual documents with charts and diagrams (these need OCR and vision models)
• Documents with complex tables (extraction accuracy drops to 60-70%)
• Scanned PDFs with poor quality (OCR errors cascade into retrieval issues)
• Information that requires synthesizing across many documents (current chunk is limited)

For POCs, stick to text-heavy documents where you'll get good results quickly. Save the hard cases for production.

From Proof-of-Concept to Production

Your afternoon project can absolutely become a production system, but you'll need to address:

• Scale: Vector databases like Pinecone or Weaviate for larger document collections. ChromaDB works fine up to ~10,000 documents. Beyond that, you'll want something more robust.

• Performance: Caching frequently asked questions. We've seen 30-40% of queries are repeats in most systems. Cache those and your costs drop significantly.

• Accuracy: Add reranking to improve result quality. This typically improves answer accuracy 10-15% but adds complexity and latency.

• Reliability: Error handling and fallback mechanisms. POCs can crash. Production systems need graceful degradation.

• Security: Access controls and audit logging. POCs are open to everyone. Production needs proper authentication.

But start simple. Get something working, show it to users, and iterate based on real feedback.

We typically build POCs in one week, then spend 4-6 weeks hardening them for production use. The POC proves value; production engineering makes it reliable.

The Real Test

You know your POC is working when:

1. Non-technical users can ask questions in plain language
2. Answers include relevant citations
3. The system admits when it doesn't know something (rather than hallucinating)
4. People prefer it to manual document searching

That's your signal to invest more.

In POC demos, we track "Would you use this?" responses. Anything above 70% is a green light. Below 50% means the documents aren't suitable or the implementation needs work.

Next Steps

Once you have a working system:

• Test it with actual users, not just yourself. We do 5-10 user tests during the POC phase.
• Track which questions it answers well and which it struggles with. Build a list of failure cases to address.
• Measure response quality and iterate on chunk size and retrieval settings. A/B test changes when possible.
• Consider adding more document types gradually. Start with one type, expand as you prove value.

The goal isn't perfection—it's proving value fast enough to justify further investment.

One client tested their POC with 15 employees. 12 said it would save them 2+ hours per week. That business case got them immediate budget for production development.

Time and Cost Expectations

Here's what a typical POC requires:

Development Time: 3-6 hours for someone experienced, 8-12 hours if you're learning

API Costs: $1-5 for embedding 50 documents, $5-10 for 100 test queries with GPT-4

Infrastructure: Free (ChromaDB runs locally)

Total cost to prove concept: under $20 and one afternoon.

Compare that to weeks of development and thousands in costs if you built from scratch. Modern tools make this fast and cheap.

Common Pitfalls

Starting Too Big Don't try to index your entire company knowledge base on day one. Start with 20-50 high-value documents. Prove the concept, then scale.

Over-Engineering POCs don't need perfect UI, advanced reranking, or production-grade error handling. They need to demonstrate value. Save optimization for later.

Wrong Documents Some document types work great for RAG, others don't. If your POC is struggling, try different documents before concluding RAG doesn't work.

No User Testing Building a POC only you test misses the point. Get it in front of potential users fast. Their feedback is the real validation.

Why This Matters

RAG systems democratize access to information. Instead of knowing where to look or who to ask, anyone can get accurate answers instantly.

We've seen support teams cut ticket resolution time 40%, sales teams close deals 25% faster with better product info, and new employees ramp up weeks faster with instant access to company knowledge.

That's worth an afternoon of your time.

Build the POC, test it with users, measure impact. If it works, you'll have the business case to build it properly. If it doesn't, you've learned what won't work with minimal investment.

That's the beauty of modern RAG—you can prove value in hours, not months.