Question 1

What is RAG?

Accepted Answer

RAG (Retrieval-Augmented Generation) retrieves relevant documents from your knowledge base and feeds them to an LLM at query time, so answers are grounded in your data rather than generic training knowledge.

Question 2

Which vector database should I use?

Accepted Answer

Pinecone is fastest to start for managed hosting. pgvector works well if you already use Postgres. Weaviate and Qdrant are strong for self-hosted setups. I choose based on scale, budget, and existing infrastructure.

Question 3

How do you measure RAG quality?

Accepted Answer

I build evaluation datasets with expected answers, track retrieval precision/recall, run LLM-as-judge scoring, and monitor user feedback in production.

Question 4

Can RAG work with real-time data?

Accepted Answer

Yes. I implement incremental indexing, webhook-triggered updates, and API-based ingestion so your knowledge base stays current.

Question 5

How long does a RAG project take?

Accepted Answer

A RAG MVP with one data source and chat UI typically ships in 3–4 weeks on average. Production systems with multi-source ingestion, hybrid search, and evaluation take 6–8 weeks on average.

Question 6

What chunk size and strategy works best for RAG?

Accepted Answer

It depends on your document types. Technical documentation often performs well with 512-1024 token chunks and overlap; FAQ and support content typically needs smaller segments. I optimize chunking, metadata, and reranking using evaluation datasets and retrieval metrics.

Question 7

Can users see which sources the AI used?

Accepted Answer

Yes. Every production RAG system I build includes source citations - links or snippets showing exactly which documents grounded each answer. This builds user trust and helps debug retrieval issues.

Question 8

How much does this cost for a startup?

Accepted Answer

I offer two options: hourly work at $35 - $55/hr depending on the complexity of your requirements, or a custom fixed-price quote after a free discovery call. The quote option is best when you have a defined scope and want a single number upfront.

Question 9

Do you sign NDAs and work with confidential data?

Accepted Answer

Yes. I routinely sign NDAs before reviewing proprietary code or data. Your documents, API keys, and business logic stay confidential. I can work within your existing security policies and VPC if required.

Question 10

How do we communicate during the project?

Accepted Answer

Weekly video demos plus async updates on Slack or email. You see working software every week, not status reports. I'm responsive across time zones - I've delivered for clients in 15+ countries.

Question 11

What is your payment structure?

Accepted Answer

For custom quotes: typically 30% upfront, 40% at midpoint, 30% on delivery. Hourly work is billed weekly or biweekly based on hours logged. Payments via bank transfer, PayPal, or platform escrow (Upwork/Fiverr) if you prefer.

Question 12

Do you provide post-launch support?

Accepted Answer

Yes. Every project includes a handoff period with documentation and knowledge transfer. Optional maintenance retainers cover bug fixes, model updates, and feature iterations after launch.

Question 13

Why hire a freelance AI engineer vs an agency?

Accepted Answer

You work directly with the person building your system - no account managers or junior devs passed off as seniors. Faster iteration, lower overhead, and 6+ years of hands-on production AI experience including published research.

RAG Development

Why teams get stuck

What you get

Pricing

Hourly rate

Custom quote

RAG Systems - common questions