RAG or Fine-Tuning? We'll Tell You Which (And Why).
Stop guessing about LLM architecture. Our data-driven framework analyzes your specific use case to recommend the optimal approach—saving you months of trial and error.
Our Decision Framework
We analyze four key factors to recommend the optimal approach
| Factor | RAG | Fine-Tuning | Our Recommendation |
|---|---|---|---|
| Data Volume | Works with any amount of data | Requires 1,000+ high-quality examples | RAG if you have <1,000 examples |
| Update Frequency | Real-time updates, no retraining | Requires retraining for updates | RAG for frequently changing data |
| Response Accuracy | 85-92% accuracy with good retrieval | 90-95% accuracy when done right | Fine-tuning for mission-critical accuracy |
| Cost Structure | $0.10-0.50 per 1K queries | $500-5K upfront + $0.05 per 1K | RAG for <10K queries/month |
RAG vs. Fine-Tuning: Complete Breakdown
Both approaches have their place. The key is choosing the right one for your specific situation.
RAG (Retrieval-Augmented Generation)
Combines your data with a pre-trained model in real-time
Best For:
Advantages
- No training required
- Real-time data updates
- Lower upfront costs
- Transparent reasoning
- Works with small datasets
Limitations
- Higher per-query costs
- Dependent on retrieval quality
- Potential latency issues
- Limited customization
Fine-Tuning
Train a model specifically on your data and use cases
Best For:
Advantages
- Highest accuracy potential
- Lower per-query costs
- Complete customization
- Faster inference
- No external dependencies
Limitations
- High upfront investment
- Requires quality training data
- Longer development time
- Difficult to update
Avoid These Costly LLM Mistakes
We've seen these mistakes cost companies 6+ months and $50K+ in wasted development.
Choosing Based on Hype
The Problem
Following trends instead of analyzing your specific use case
Our Solution
Use our data-driven decision framework
Impact
Avoid 6-month rebuilds and wasted budget
Underestimating Data Quality
The Problem
Assuming any data will work for fine-tuning
Our Solution
Comprehensive data audit and preparation
Impact
Achieve 90%+ accuracy from day one
Ignoring Operational Costs
The Problem
Only considering development costs, not ongoing expenses
Our Solution
Full TCO analysis over 12-24 months
Impact
Avoid budget surprises and cost overruns
Our LLM Architecture Process
From analysis to production deployment, we ensure you get the right architecture for your needs.
Data & Use Case Analysis
Week 1
We analyze your data quality, volume, and specific use cases to determine the optimal approach.
Deliverables:
Architecture Design
Week 2
Design the optimal LLM architecture based on your requirements and constraints.
Deliverables:
Proof of Concept
Weeks 3-4
Build and test a working prototype to validate the approach before full implementation.
Deliverables:
Production Implementation
Weeks 5-8
Build, deploy, and optimize your production LLM system with monitoring and maintenance.
Deliverables:
Stop Guessing About LLM Architecture
Get a data-driven recommendation for your specific use case. No generic advice, just what works for you.
Frequently Asked Questions (FAQs)
What does LLM architecture consulting include?
We design and build production LLM systems: model selection, retrieval-augmented generation (RAG) pipelines, prompt and context management, evaluation harnesses, cost controls, and fallback strategies. The goal is an AI feature that is reliable, measurable, and affordable at scale—not a demo that breaks in production.
Can you fix an AI app that works in demos but fails with real users?
Yes, this is one of our most common engagements. Typical causes are missing evaluation, unbounded context, no retry/fallback logic, and prompts that were never tested against real data. We audit the pipeline, add observability and evals, then harden each stage until quality is consistent.
How do you control LLM API costs in production?
We combine model routing (cheaper models for simple tasks), caching, prompt compression, batching, and usage monitoring with per-feature budgets. Most teams we work with cut inference costs 40-70% without degrading output quality.
Which LLM providers and frameworks do you work with?
We work with Anthropic Claude, OpenAI, and open-weight models, orchestrated through frameworks or plain SDKs depending on what the system needs. We are vendor-neutral: the architecture is designed so you can switch or mix providers without a rewrite.
Do you build AI agents and automation workflows?
Yes. We build agentic systems that take real actions—processing documents, updating CRMs, handling support triage—with the guardrails production systems need: permission boundaries, audit logs, human-in-the-loop checkpoints, and deterministic fallbacks.