Source of unstructured PDF/Text files
AI Models & Assistants / Knowledge library
GPT-4 Turbo Preview
An older fast GPT model
Why learn GPT-4 Turbo Preview?
Offers the documented OpenAI model capabilities through hosted.
The problem it solves
Provides model inference for supported text inputs.
Where it came from
GPT-4 Turbo Preview: An older fast GPT model Published aliases: gpt-4-0125-preview, gpt-4-1106-vision-preview. Input: text. Output: text. Checked 2026-10-13. Model capabilities are distinct from tools available in a particular agent.
Build your understanding
Work through these exercises in order. Try the task before opening a reference.
- Explain what GPT-4 Turbo Preview does.
- Identify a use case for GPT-4 Turbo Preview.
- Describe a limitation of GPT-4 Turbo Preview.
- Name a compatible tool for GPT-4 Turbo Preview.
- Compare GPT-4 Turbo Preview against an alternative.
Documentation and sources
- Primary source / model card (developers.openai.com)
Catalog research notes
- See the AI Workbench for source dates, pricing evidence and configuration details.
Connect the ideas
Built on
Real World Example
Legacy Legal Document Summarization
A mid-sized law firm managing thousands of archived case files.
The business problem
The firm struggles with high operational costs and slow retrieval times when associates manually review lengthy, unstructured legal briefs to identify key case precedents.
How this topic solves it
By leveraging the 128,000 token context window of the gpt-4-turbo-preview model, the firm can ingest entire multi-page legal documents in a single API call, allowing the model to synthesize complex arguments and extract relevant precedents without needing to chunk the text into smaller, potentially disjointed segments.
- Configure the application to connect to the Chat Completions endpoint using the gpt-4-0125-preview snapshot.
- Pre-process legal documents into plain text format to ensure compatibility with the text-only input modality.
- Construct a system prompt that defines the extraction schema for case precedents.
- Send the full document text to the API, ensuring the total token count remains within the 128,000 window.
- Parse the structured JSON response to update the firm's internal case management database.
This flow illustrates how raw legal text is transformed into actionable insights using the model.
Payload containing full text
Processing engine for summarization
Storage for extracted precedents
- Legal Archive → Ingestion → API Request
- API Request → 128k Context Window → GPT-4 Turbo Preview
- GPT-4 Turbo Preview → Structured Output → Database
Implementation example
This code demonstrates calling the Chat Completions endpoint to process a full legal brief in one request, utilizing the large context window.
client.chat.completions.create(
model='gpt-4-0125-preview', # Using the specific snapshot for consistency
messages=[{'role': 'user', 'content': full_legal_brief}] # Sending entire document at once
)Expected business outcome
Associates experience a significant reduction in time spent on initial document triage, enabling them to focus on higher-value legal strategy.
Limitations and checks
The model has a knowledge cutoff of December 2023, meaning it cannot natively understand or reference legal developments or case law established after this date; developers must verify the accuracy of citations against current legal databases.