Question 1

How do you choose an LLM provider?

Accepted Answer

It depends on task type, latency, context length, and cost. I evaluate your use case against provider capabilities and often design routing so different request types hit the best-fit API rather than locking into one vendor upfront.

Question 2

How do you control LLM API costs?

Accepted Answer

I implement token budgets per user, response caching, model routing (lighter models for simple tasks), and usage dashboards so costs stay predictable.

Question 3

Can you add a copilot to our existing React or Next.js app?

Accepted Answer

Yes. I embed streaming chat UIs, command palettes, and inline AI suggestions into existing frontends via API routes or WebSocket connections - without rewriting your app.

Question 4

Should I fine-tune or use RAG for my use case?

Accepted Answer

RAG is almost always the first choice - it's cheaper, updatable, and citeable. Fine-tuning makes sense for consistent tone/format or domain-specific language patterns. I recommend based on your data and evaluation goals.

Question 5

Can you integrate open-source models?

Accepted Answer

Yes. I deploy via Ollama, vLLM, Together AI, or Groq for cost-sensitive workloads. Hybrid setups route simpler queries to self-hosted or hosted open-source models and heavier tasks to commercial APIs when needed.

Question 6

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 7

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 8

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 9

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 10

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 11

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.

LLM Integration

Why teams get stuck

What you get

Pricing

Hourly rate

Custom quote

LLM Integration - common questions