OpenAI Announces GPT-4.5 Turbo with RAG-Optimized Architecture

Announcement

OpenAI has unveiled GPT-4.5 Turbo, an intermediate release between GPT-4 and GPT-5, with features specifically designed for retrieval-augmented generation workflows.

Key Features

Native Retrieval Mode

GPT-4.5 includes built-in retrieval without external vector databases:

DEVELOPERpython
response = openai.chat.completions.create(
    model="gpt-4.5-turbo",
    messages=[{"role": "user", "content": "What is our refund policy?"}],
    retrieval_sources=[
        {"type": "file", "file_id": "file-abc123"},
        {"type": "url", "url": "https://example.com/docs"}
    ],
    retrieval_mode="automatic"  # or "manual" for custom control
)

How it works:

• OpenAI indexes provided files/URLs
• Retrieval happens during generation
• No separate vector database needed

Limitations:

• Max 50 files or URLs per request
• Files must be < 50MB each
• Updated files require re-indexing

Structured Output Mode

Generate JSON responses that conform to schemas:

DEVELOPERpython
response = openai.chat.completions.create(
    model="gpt-4.5-turbo",
    messages=[{"role": "user", "content": query}],
    response_format={
        "type": "json_schema",
        "json_schema": {
            "name": "rag_response",
            "schema": {
                "type": "object",
                "properties": {
                    "answer": {"type": "string"},
                    "sources": {
                        "type": "array",
                        "items": {
                            "type": "object",
                            "properties": {
                                "title": {"type": "string"},
                                "page": {"type": "integer"},
                                "quote": {"type": "string"}
                            }
                        }
                    },
                    "confidence": {"type": "number"}
                }
            }
        }
    }
)

Benefits:

• Guaranteed valid JSON
• No parsing errors
• Consistent citation format

Improved Context Utilization

Better at using long contexts:

• 128K token window (unchanged)
• 40% better "needle in haystack" performance
• Maintains accuracy across full context length

Benchmark results:

Performance Improvements

Speed

• 30% faster than GPT-4 Turbo
• Median latency: 1.2s (down from 1.7s)
• Supports up to 500 tokens/second streaming

Cost Reduction

Pricing optimized for RAG:

50% cost reduction while maintaining GPT-4 level quality.

Quality

Tested on RAG-specific benchmarks:

Consistent 5-7% improvement across datasets.

RAG-Specific Capabilities

Citation Generation

Automatic citation insertion:

DEVELOPERpython
response = openai.chat.completions.create(
    model="gpt-4.5-turbo",
    messages=[...],
    enable_citations=True  # New parameter
)

# Response includes inline citations
print(response.choices[0].message.content)
# "The refund policy allows returns within 30 days[1] for a full
# refund[2]."

# Citations provided separately
for citation in response.citations:
    print(f"[{citation.id}] {citation.source}: {citation.quote}")

Factuality Scoring

Self-assessment of answer confidence:

DEVELOPERpython
response = openai.chat.completions.create(
    model="gpt-4.5-turbo",
    messages=[...],
    include_confidence=True
)

print(response.confidence_score)  # 0.0-1.0
# 0.9 = High confidence
# 0.5 = Uncertain
# 0.2 = Low confidence, likely hallucination

Useful for filtering low-quality responses.

Multi-Turn Context Management

Better conversation handling:

• Automatic summarization of old turns
• Smart context truncation
• Maintains coherence across long conversations

Migration Guide

From GPT-4 Turbo

Minimal changes required:

DEVELOPERpython
# Before
response = openai.chat.completions.create(
    model="gpt-4-turbo-preview",
    messages=messages
)

# After
response = openai.chat.completions.create(
    model="gpt-4.5-turbo",  # Updated model
    messages=messages
)

Enabling New Features

DEVELOPERpython
response = openai.chat.completions.create(
    model="gpt-4.5-turbo",
    messages=messages,

    # Optional: Built-in retrieval
    retrieval_sources=[...],

    # Optional: Structured output
    response_format={"type": "json_schema", ...},

    # Optional: Citations
    enable_citations=True,

    # Optional: Confidence scores
    include_confidence=True
)

Use Cases

Customer Support

• Built-in retrieval over documentation
• Structured responses for consistent formatting
• Citation for answer verification

Research Assistants

• Retrieval across multiple papers
• Confidence scoring for fact-checking
• Long context for comprehensive analysis

Enterprise Knowledge Management

• Indexed internal documentation
• Structured extraction of information
• Cost-effective at scale

Limitations

Built-in Retrieval

• Limited to 50 sources per request
• No fine-grained control over chunking
• Cannot update files without re-upload
• Not suitable for very large document collections

Recommendation: Use traditional RAG (vector DB) for:

• Large document collections (> 10K docs)
• Frequently updated content
• Custom chunking strategies
• Advanced retrieval (hybrid search, reranking)

Structured Output

• Adds ~10-15% latency
• Max schema complexity: 10 nested levels
• Cannot mix structured and unstructured outputs

Pricing Calculator

Example cost comparison:

Scenario: 10K queries/day, 2K input tokens, 500 output tokens each

GPT-4.5 Turbo offers GPT-4 quality at half the cost.

Availability

• Generally available via OpenAI API
• Rolling out to Azure OpenAI (November)
• ChatGPT Plus/Team users (select GPT-4.5)
• Enterprise customers (immediate access)

Best Practices

1. Use built-in retrieval for small doc sets (< 100 files)
2. Enable citations for transparency
3. Check confidence scores for quality control
4. Structured output for consistent parsing
5. Monitor token usage to optimize costs

Conclusion

GPT-4.5 Turbo represents OpenAI's commitment to making RAG more accessible and cost-effective. While built-in retrieval won't replace vector databases for complex applications, it significantly lowers the barrier to entry for simpler RAG use cases.