RAG vs Fine-Tuning: Choosing the Right LLM Approach for Your Product
Both RAG and fine-tuning improve LLM performance on your specific use case — but they solve different problems. Here's how to choose.
GenAI Engineering$18K – $40K
Generative AI Engineering
GenAI that holds up past the demo.
<2s
Response latency target
95%+
Retrieval accuracy achieved
6–10w
Typical timeline
100%
Source-traceable outputs
THE CHALLENGE
Sound familiar?
- Your LLM prototype works in testing but breaks with real users
- Hallucinations make AI outputs unreliable for your specific use case
- You don't know how to connect the LLM to your actual product data
- Response latency is too high — AI answers are too slow for your UX
- Prompt engineering that worked in the demo doesn't scale to a production system
OUR APPROACH
How we solve it
We build production-ready generative AI systems with proper RAG pipelines, context management, output validation, and infrastructure designed for real users. Not just an API wrapper — a system that behaves predictably at scale, with every response traceable to a source.
What you receive
- Production RAG pipeline with chunking, embedding, and retrieval logic
- LLM orchestration layer with fallbacks and retry logic
- Context window management and memory architecture
- Output validation and guardrails system
- Streaming API endpoint with latency optimization
- Evaluation framework: accuracy, latency, and coherence benchmarks
- Prompt versioning and management system
OUTCOMES
What you walk away with
01
Reliable AI responses grounded in your actual content
02
Hallucination rate reduced through retrieval and validation layers
03
Response latency under 2 seconds for most query types
04
Every response traceable to a source — fully auditable
05
Architecture that handles usage spikes without degradation
OUR DIFFERENCE
Why GenAI Engineering from Virinchi
Production-first engineering
Every system is built to production standards from day one — no notebook demos, no prototypes handed off as products.
Evaluation-driven delivery
We agree on accuracy, latency, and hallucination rate benchmarks before building. Delivery is measured against those targets.
Domain-specific expertise
We've shipped GenAI for FinTech, HealthTech, eCommerce, and Industrial clients across four markets. We know your sector's constraints.
USE CASES
Real-world applications
Enterprise Knowledge Assistants
Retrieval-augmented chatbots that answer accurately from proprietary company knowledge bases.
AI-Powered Content Workflows
Automated generation, review, and publishing pipelines for content-heavy teams.
Code Generation Tools
LLM-powered developer tools integrated into engineering workflows for productivity gains.
HOW IT WORKS
Our delivery process
01
Use Case Definition
Define query types, context sources, acceptable outputs, and latency targets before building anything.
02
Data Ingestion Pipeline
Parse, chunk, embed, and index your content into a retrieval-ready vector store.
03
Retrieval Architecture
Build and evaluate retrieval quality — precision, recall, and relevance scoring.
04
LLM Integration
Orchestration, context assembly, output formatting, and safety guardrails.
05
Evaluation & Hardening
Adversarial testing, latency optimization, monitoring setup, and production deployment.
Is this right for you?
Best suited for
- Startups building AI-native products with LLMs at the core
- Teams who've tried GPT APIs but can't make results production-reliable
- Companies that need AI over their own documents, databases, or knowledge bases
- Founders who need reliable, auditable AI outputs
Not the right fit for
- Projects where a simple prompt + API call produces reliable results
- Teams with no structured data to ground the model in
- Companies requiring zero-error AI regardless of input quality
Engineering Stack
Built with the tools that matter
38 production-grade technologies — every one battle-tested in shipped products.
INVESTMENT
Engagement & pricing
$18K – $40K
6–10 weeks
- Phased delivery: ingestion → retrieval → LLM integration → hardening
- Evaluation benchmarks agreed upfront and measured at delivery
- Post-launch support window included in all engagements
Ready to start?
Get a detailed proposal within 48 hours. No commitment required.
Frequently Asked Questions
Didn't find what you were searching for? Reach out to us at [email protected] and we'll assist you promptly.
Retrieval-Augmented Generation (RAG) combines an LLM with a search layer over your private data. If your users need accurate, up-to-date answers from proprietary content, you need RAG.
We build evaluation pipelines, output validation, and human feedback loops into every GenAI system. Reliability is engineered, not assumed.
We work with all major foundation models — Claude, GPT-4o, Gemini, Llama 3, Mistral — and select the right model for your use case, latency requirements, and cost targets.
FROM OUR CLIENTS
Built with teams who ship
The team took our AI concept from whiteboard to production in 10 weeks. The architecture they designed handles 10x our expected load with no issues.
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READY TO START?