Capabilities of Claude Sonnet 3.5+

Claude 3.5 Sonnet represents the current state-of-the-art for production multimodal RAG

systems. Let's explore why.

Claude Model Family

graph LR
    A[Claude 3 Family] --> B[Haiku]
    A --> C[Sonnet]
    A --> D[Opus]
    
    B --> E[Fast & Cheap]
    C --> F[Balanced]
    D --> G[Maximum Quality]
    
    style C fill:#d4edda

Claude 3.5 Sonnet:

Sweet spot for production RAG
High accuracy at reasonable cost
Excellent vision capabilities
200K context window
Strong instruction following

Vision Understanding

###1. Document Analysis

Claude excels at understanding complex documents:

# Conceptual: Analyzing a financial report
response = claude.analyze({
    "image": "Q4_financial_report.pdf_page_1.png",
    "prompt": "Extract key metrics from this financial statement"
})

# Claude can:
# - Read tables accurately
# - Understand chart legends
# - Parse multi-column layouts
# - Extract footnotes and references

Strengths:

✅ Accurate table extraction
✅ Multi-column text flow
✅ Footnote association
✅ Chart data reading
✅ Diagram interpretation

2. Code from Screenshots

Input: Screenshot of UI mockup
Claude Output:
"""
<div className="card">
  <img src="/product.jpg" alt="Product" />
  <h2>Product Name</h2>
  <p className="price">$99.99</p>
  <button>Add to Cart</button>
</div>

CSS:
.card {
  border: 1px solid #ddd;
  border-radius: 8px;
  padding: 16px;
  box-shadow: 0 2px 4px rgba(0,0,0,0.1);
}
"""

3. Technical Diagrams

Can interpret and explain:

Architecture diagrams
Flowcharts
Network topologies
UML diagrams
Circuit schematics

Input: Architecture diagram
Claude: "This diagram shows a microservices architecture with:
- API Gateway handling external requests
- 3 backend services (Auth, Orders, Payments)
- Redis cache layer
- PostgreSQL database with read replicas
- Message queue (RabbitMQ) for async processing"

Long Context Understanding

graph TD
    A[200K Token Context] --> B[~150K Words]
    B --> C[~300 Pages]
    
    D[What Fits] --> E[Full Novel]
    D --> F[Large Codebases]
    D --> G[Long Transcripts]
    D --> H[Multiple Documents]
    
    style A fill:#d4edda

Practical Applications

Single Document Analysis:

- 300-page technical manual
- Full codebase (~50K lines)
- 3-hour meeting transcript
- Year's worth of emails

Multi-Document RAG:

Retrieve 20 documents × 10K tokens each = 200K tokens
Claude can reason across ALL retrieved content

Context Window Strategy

# Conceptual: Maximizing context usage
context_strategy = {
    "retrieved_docs": 15,  # ~120K tokens
    "query": "...",  # ~500 tokens
    "system_prompt": "...",  # ~2K tokens
    "examples": 3,  # ~10K tokens
    "buffer": "~67K tokens remaining"
}

# Claude can:
# - Process all 15 docs simultaneously
# - Find connections across documents
# - Synthesize comprehensive answers

Accuracy and Reliability

Benchmark Performance

Claude 3.5 Sonnet leads in:

MMLU (Massive Multitask Language Understanding): 88.7%
Graduate-level reasoning (GPQA): 59.4%
Math problem-solving (MATH): 71.1%
Coding (HumanEval): 92.0%

Hallucination Resistance

graph LR
    A[Query] --> B[Claude 3.5]
    B --> C{Certain?}
    C -->|Yes| D[Provide Answer]
    C -->|No| E[Express Uncertainty]
    
    E --> F["I don't have enough information..."]
    
    style E fill:#fff3cd
    style F fill:#d4edda

Claude is trained to:

Decline when uncertain: "I cannot verify this claim"
Cite reasoning: Explain its thought process
Request clarification: Ask followup questions

RAG-Specific Strengths

1. Source Attribution

Claude excels at tracking information sources:

# Conceptual response format
{
    "answer": "The revenue increased by 15% in Q4 2025.",
    "reasoning": "This is based on the financial table on page 3, 
                  which shows Q3: $2.0M and Q4: $2.3M.",
    "sources": [
        {"doc": "Q4_report.pdf", "page": 3, "section": "Financial Summary"}
    ]
}

2. Multi-Document Synthesis

Can combine information from multiple retrieved documents:

Retrieved:
- Doc 1: "Product X costs $99"
- Doc 2: "Q4 pricing has 20% discount"
- Doc 3: "Free shipping over $75"

Claude Synthesis:
"Product X is currently $79.20 (20% Q4 discount from $99) 
 and qualifies for free shipping since it's over $75."

3. Structured Output

Perfect for RAG systems needing structured data:

{
    "product_name": "Widget Pro",
    "price": 99.99,
    "features": ["waterproof", "rechargeable", "bluetooth"],
    "availability": "in_stock",
    "sources": ["catalog_2025.pdf", "inventory_db"]
}

API and Integration

AWS Bedrock

# Conceptual: Claude via Bedrock
import boto3

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

response = bedrock.invoke_model(
    modelId='anthropic.claude-3-5-sonnet-20241022-v2:0',
    body={
        "messages": [...],
        "max_tokens": 4096,
        "temperature": 0.0,  # Deterministic for RAG
        "top_p": 1.0
    }
)

Bedrock Advantages:

Managed infrastructure
Built-in security (IAM, VPC)
Pay-per-use pricing
No API key management
Integration with Knowledge Bases

Direct API (Anthropic)

# Conceptual: Direct API usage
import anthropic

client = anthropic.Client(api_key=os.environ["ANTHROPIC_API_KEY"])

response = client.messages.create(
    model="claude-3-5-sonnet-20241022",
    max_tokens=4096,
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Analyze this image:"},
                {"type": "image", "source": {"type": "base64", "data": image_b64}}
            ]
        }
    ]
)

Cost and Performance Profile

Pricing (as of 2026)

Input: $3 / 1M tokens
Output: $15 / 1M tokens

Example RAG Query:
- Retrieved context: 50K tokens ($0.15)
- Response: 1K tokens ($0.015)
- Total: ~$0.165 per complex query

Latency Characteristics

graph LR
    A[Query] --> B[Retrieval: 100-500ms]
    B --> C[Claude Generation: 2-5s]
    C --> D[Total: 2.5-5.5s]
    
    E[Streaming] --> F[First Token: &lt;300ms]
    F --> G[Better UX]

Optimization:

Use streaming for better perceived latency
Cache system prompts (reduces input tokens)
Batch similar queries when possible

Best Practices for RAG

1. Temperature Settings

# For RAG, use low temperature
temperature = 0.0  # Deterministic, fact-based

# vs

temperature = 1.0  # Creative, variable (not for RAG)

2. System Prompts

system_prompt = """
You are a helpful assistant that answers questions based ONLY 
on the provided context.

Rules:
1. If the answer isn't in the context, say you don't know
2. Cite specific sections when answering
3. Never make up information
4. If context is ambiguous, ask for clarification
"""

3. Structured Prompts

user_prompt = f"""
Context:
{retrieved_documents}

Question: {user_question}

Please answer based only on the provided context. 
Include source citations.
"""

Limitations

1. No Real-Time Data

Claude cannot:

Browse the web
Access APIs
Query databases
Get current time (beyond training cutoff)

Solution: RAG retrieval layer handles this

2. Image Generation

Claude vision is analysis only, not generation.

Cannot:

Create images
Edit photos
Generate diagrams

Solution: Use separate models (DALL-E, Midjourney) if needed

3. Audio/Video Input

Currently text + images only.

For audio/video:

Transcribe with Whisper
Extract key frames
Feed to Claude as text + images

Comparison with Alternatives

Feature	Claude 3.5	GPT-4V	Gemini 1.5
Vision Quality	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐
Context Window	200K	128K	1M+
Accuracy	Highest	High	High
Cost	Medium	Higher	Lower
RAG Optimization	Excellent	Good	Good
Safety	Strong	Strong	Strong

Bottom Line: Claude 3.5 Sonnet is the best choice for production multimodal RAG due to its balance of accuracy, cost, and RAG-specific features.

In the next lesson, we'll compare local vs hosted model deployments.