How do I handle long-context sequences when using GPT-3 for generating legal documents

Question

Can you tell me How do I handle long-context sequences when using GPT-3 for generating legal documents?

Answer 1

To handle long-context sequences in GPT-3 for legal documents, use chunking, summarization, or vector embeddings to manage and retrieve relevant content efficiently.

Here is the code snippet you can refer to:

In the above code we are using the following key points:

• Chunking – Splits long legal text into smaller sections to stay within token limits.
• Summarization – Uses GPT-3 to condense each chunk while retaining key legal information.
• Automated Processing – Iterates through all chunks and reconstructs a manageable output.
• Optimized API Usage – Ensures efficient token management and prevents overflow errors.
• Scalability – Can be extended with embeddings for semantic search in large legal corpora.

Hence, by combining chunking, summarization, and token management, GPT-3 can effectively process long legal documents while maintaining coherence and accuracy.

Related Post: How to handle context window limitations when generating long text with GPT models