Fine-Tuning LLMs with Amazon Bedrock: A Practical Guide

While foundation models (FMs) like Anthropic's Claude are incredibly powerful out of the box, their true potential is unlocked when you customize them for your specific domain or task. Amazon Bedrock provides two primary methods for this: Retrieval Augmented Generation (RAG) and fine-tuning.

This guide focuses on fine-tuning, the process of training a model on your own dataset to adapt its behavior, style, and knowledge.

Fine-Tuning vs. RAG: Which to Choose?

First, it's important to understand the difference:

• Retrieval Augmented Generation (RAG): This technique provides the model with external knowledge at inference time. You retrieve relevant documents from a knowledge base (like a vector database) and include them in the prompt. This is best for teaching the model new facts or providing it with up-to-date information.

• Fine-Tuning: This process updates the model's internal weights by training it on a dataset of examples. This is best for teaching the model a new skill, style, or format. For example, you could fine-tune a model to always respond in a specific JSON format or to adopt the writing style of your company's brand.

Use Case	Best Approach
Answering questions about recent events	RAG
Summarizing internal legal documents	RAG
Generating code in a specific language	Fine-tuning
Classifying customer support tickets	Fine-tuning
Adopting a specific brand voice	Fine-tuning

Often, the best results come from combining both techniques.

How to Fine-Tune a Model in Bedrock

The process involves preparing a dataset, creating a fine-tuning job, and then using the custom model.

Step 1: Prepare Your Dataset

Your dataset is the most critical component. It must be in a JSON Lines (.jsonl) format, where each line is a JSON object containing a prompt and completion pair.

Example dataset.jsonl for sentiment analysis:

{"prompt": "This movie was fantastic, the acting was superb!", "completion": "Positive"}
{"prompt": "The product broke after one use, very disappointing.", "completion": "Negative"}
{"prompt": "It works as expected, nothing special.", "completion": "Neutral"}

• Quality over Quantity: A smaller, high-quality dataset is better than a large, noisy one.
• Follow the Prompt Format: The prompt should be formatted exactly as you would when querying the base model.
• Upload to S3: Once your dataset is ready, upload it to an S3 bucket.

Step 2: Create a Fine-Tuning Job

You can do this via the AWS Console or the Boto3 SDK.

Using Boto3 in Python:

import boto3

bedrock = boto3.client('bedrock')

# Define the job parameters
job_name = 'my-sentiment-analyzer-job'
custom_model_name = 'my-sentiment-analyzer-v1'
base_model_id = 'amazon.titan-text-express-v1' # Or another supported model

training_data_uri = 's3://my-bedrock-datasets/training/dataset.jsonl'

response = bedrock.create_model_customization_job(
    jobName=job_name,
    customModelName=custom_model_name,
    roleArn='arn:aws:iam::123456789012:role/BedrockFineTuningRole', # A role with S3 and Bedrock permissions
    baseModelIdentifier=base_model_id,
    hyperParameters={
        'epochCount': '1',
        'batchSize': '16',
        'learningRate': '0.00001'
    },
    trainingDataConfig={'s3Uri': training_data_uri},
    outputDataConfig={'s3Uri': f's3://my-bedrock-models/output/{job_name}'}
)

print(f"Fine-tuning job started with ARN: {response['jobArn']}")

This kicks off an asynchronous job. You can monitor its progress in the Bedrock console.

Step 3: Use Your Custom Model

Once the job is complete, you'll have a new custom model available in your account. You can use it with the same invoke_model API call as before, but you'll use the ARN of your provisioned custom model.

First, you need to purchase Provisioned Throughput for your custom model. This reserves capacity for your model and ensures it's always ready to serve inferences.

# Purchase Provisioned Throughput (this has a cost)
bedrock.create_provisioned_model_throughput(
    modelName='my-sentiment-analyzer-v1',
    provisionedModelName='my-provisioned-sentiment-model',
    modelId='arn:aws:bedrock:us-east-1:123456789012:custom-model/my-sentiment-analyzer-v1',
    commitmentDuration='OneMonth',
    modelUnits=1
)

Once the provisioned throughput is active, you can invoke it:

bedrock_runtime = boto3.client('bedrock-runtime')

# The modelId is now the ARN of your provisioned model
provisioned_model_arn = 'arn:aws:bedrock:us-east-1:123456789012:provisioned-model/...' 

prompt = "The customer service was incredibly helpful and resolved my issue quickly."

body = json.dumps({"inputText": prompt})

response = bedrock_runtime.invoke_model(
    body=body,
    modelId=provisioned_model_arn
)

response_body = json.loads(response.get('body').read())
# The output structure depends on the base model used
print(response_body['results'][0]['outputText'])
# Expected Output: Positive

Conclusion

Fine-tuning is a powerful technique for adapting foundation models to your specific needs. By training a model on your own data, you can teach it new skills, styles, and formats that go beyond what's possible with prompt engineering alone. While RAG is often the right choice for providing factual knowledge, fine-tuning is the key to truly customizing a model's behavior. With Amazon Bedrock, this process is more accessible than ever, allowing you to build highly differentiated AI applications.