What is AI Engineering?
The discipline of deploying AI models (like GPT-4, Claude) into real-world apps with reliability, speed, and scalability.
RAG (Retrieval-Augmented Generation) Explained
Combines LLMs with real-time data (your PDFs, databases) to reduce hallucinations by 60%+.
| Without RAG | With RAG |
|---|---|
| Generic responses | Data-grounded answers |
| $10/1M tokens | $4/1M tokens (60% savings) |
RAG: Your Company's AI Research Assistant
Imagine an employee who could instantly reference all your documents, emails, and databases before answering any question. That's what Retrieval-Augmented Generation (RAG) does for AI systems.
Unlike standard chatbots that rely only on their training data, RAG systems:
- Search your specific documents first
- Combine this with general knowledge
- Generate accurate, up-to-date responses
"It's like giving ChatGPT access to your filing cabinet while maintaining privacy controls."
LLM Fine-Tuning: Teaching AI Your Business Language
While RAG handles knowledge, fine-tuning shapes how the AI communicates:
We can adjust models like GPT-4 or Claude to:
- Match your brand voice (formal, casual, technical)
- Understand industry-specific terminology
- Follow your compliance requirements automatically
Example: A law firm's AI automatically uses proper legal citations without being prompted.
Fine-Tuning vs. RAG: When to Use Which?
Fine-Tuning
- Best for domain-specific behavior
- Cost: $5k–$50k
- Ideal for: Brand voice, compliance rules
RAG
- Best for dynamic knowledge
- Cost: $2k–$20k
- Ideal for: Real-time data, multi-source
AI Engineering Cost Breakdown
Fine-Tuning
$10k–$100k
- Data cleaning
- Cloud GPUs
- Evaluation
RAG Pipeline
$8k–$30k
- Vector DB
- Chunking logic
- Caching
The Hidden Costs of AI Engineering
- Token economics: $0.50–$10/1M tokens
- Cold-start latency: 5s → 800ms optimization
- Compliance: GDPR/HIPAA-ready architectures
5 Cutting-Edge AI Apps You Can Build
- Self-Updating Knowledge Base (RAG + Claude Opus)
- AI Compliance Auditor (Fine-tuned Mistral)
- Personalized Sales Copilot (RAG + CRM data)
- Multilingual Voice Assistant (Whisper + GPT-4)
- Automated Code Reviewer (RAG + codebase)
4-Step AI MVP Process
1. Data Audit
Is RAG or fine-tuning better?
2. Architecture
Open-source vs proprietary?
3. Optimization
30–70% token cost reduction
4. Deployment
Serverless, on-prem, hybrid
"Reduced fintech startup's LLM costs by 60% using RAG + fine-tuned Mistral, cutting response time from 5s to 800ms."
Why Most AI Projects Fail
Teams focus on model accuracy, not:
- Token economics
- Cold-start latency
- Production scalability
LLM Cost/Performance Tradeoffs
| Model | Cost/1M tokens | Best For |
|---|---|---|
| GPT-4/Claude | $10 | Out-of-the-box quality |
| Mistral/Llama 3 | $0.50 | Custom fine-tuning |
Your AI Roadmap
Phase 1: RAG MVP
4–8 weeks
Phase 2: Fine-Tuning
+2–4 weeks
Phase 3: Edge AI
On-device deployment
Transparent Pricing
Starter RAG MVP
$15k–$30k
Launch in 6 weeks
Enterprise AI
$50k–$200k
Full fine-tuning + compliance
Ready to Build?
- Free 30-min architecture review
- We propose cost/time estimate
- Build & deploy in weeks
Only 2 MVP slots left this month
FAQ
Can we switch LLMs later?
Yes, we design modular pipelines.
Do you handle compliance?
Yes, GDPR/HIPAA-ready architectures.