TL;DR
I am reading book Build LLM from scratch by Sebastian Raschka. In previous post we covered the topic what is the primary function of a llm. Today’s topic is next question from chapter 1, What is main advantage of using custom-built LLMs over general-purpose LLMs?
Custom built LLM
What is the main advantage of using custom-built LLMs over general-purpose LLMs? They can outperform general-purpose LLMs in specific tasks or domains.
I asked ChatGpt following prompt:
Please explain what is general-purpose llm, what is custom build llm and why custom build llm could outperform general-purpose LLM in specific tasks or domains. Please provide examples.
1️⃣ What is a general-purpose LLM?
A general-purpose LLM is trained to perform many different language tasks across many domains.
Characteristics
- Trained on very large, diverse datasets
- Designed to be a jack-of-all-trades
Handles:
- Q&A
- Summarization
- Coding
- Translation
- Creative writing
- Must be safe, neutral, and broadly correct
Examples
- OpenAI GPT-4 / ChatGPT
- Google Gemini
- Meta LLaMA (base models)
Strengths
- Very flexible
- Good general knowledge
- Strong reasoning across many topics
Weaknesses
- Not optimized for any single domain
- Can be overly verbose
- May avoid specifics due to safety tuning
- Cannot use proprietary knowledge
What made gpt-3 large model. Training cost was 4.6 million usd, training corpus has 410 billion tokens, but only 300 billion were used.
2️⃣ What is a custom-built LLM?
A custom-built LLM is a model that is trained or fine-tuned for a specific task, domain, or audience.
This can mean:
- Training from scratch on domain-specific data
- Fine-tuning an existing base model
- Applying domain-specific instruction tuning
- Adding domain-specific vocabulary and structure
Characteristics
- Narrow focus
- Uses domain-specific language
- Optimized for one or few tasks
- Often smaller and cheaper to run
Examples of custom LLMs
🏥 Medical domain
Trained on:
- Clinical notes
- Medical guidelines
- Research papers
Task:
- Assist doctors with diagnoses
- Summarize patient records
➡️ Often outperforms general LLMs in medical accuracy.
⚖️ Legal domain
Trained on:
- Contracts
- Case law
- Regulations
Task:
- Contract analysis
- Legal research
➡️ Understands legal phrasing better than a general model.
🧪 Internal company LLM
Trained on:
- Internal documentation
- Codebases
- Support tickets
Task:
- Internal Q&A
- Incident analysis
➡️ Can answer questions a general LLM cannot, because it lacks access to internal data.
3️⃣ Why can custom LLMs outperform general-purpose LLMs?
Reason 1: Domain-specific data
General LLMs see everything.
Custom LLMs see exactly what matters.
Example:
- General LLM: knows some medicine
- Medical LLM: trained on only medicine
➡️ Less noise, more signal.
Reason 2: Specialized vocabulary & patterns
Domains have:
- Jargon
- Abbreviations
- Structured language
Example:
"BP dropped after ACE inhibitor"
A medical LLM understands this immediately.
A general LLM might, but with more uncertainty.
Reason 3: Task-specific optimization
Custom models can be optimized for:
- Accuracy over creativity
- Short answers over verbosity
- Deterministic outputs
Example:
Customer support LLM trained only to classify tickets
Much faster and more accurate than a general model
Reason 4: Control and safety
General LLMs must be:
- Globally safe
- Politically neutral
- Broadly aligned
Custom LLMs can:
- Follow internal rules
- Use company-specific policies
- Avoid unnecessary refusals
Reason 5: Cost and performance
Custom models are often:
- Smaller
- Faster
- Cheaper
- Easier to deploy locally
You don’t need a 175B-parameter model to:
- Classify documents
- Extract fields
- Answer internal FAQs
4️⃣ Concrete comparison example
Task: classify insurance claims
| Model | Result |
|---|---|
| General LLM | Good, but inconsistent |
| Custom LLM | Higher accuracy, consistent decisions |
Why?
- Trained on thousands of real claims
- Knows domain rules
- Learns edge cases
5️⃣ Important nuance
Custom LLMs do not replace general-purpose LLMs.
They complement them.
- General LLM → exploration, creativity, reasoning
- Custom LLM → precision, reliability, domain expertise
Conclusion
LLMs are worthless without training data. We also learned that it is possible to create smaller LLMs for specific tasks that can be run locally on our laptops. Next post will be about famous Transformer architecture.
