The maximum length or complexity of a question or prompt for DeepResearch depends on the underlying AI model’s token limit. Most modern language models, such as GPT-4, process input in chunks called tokens (roughly 4 characters of text). For example, a model with a 128k-token context window can handle prompts up to that size, but DeepResearch may impose lower limits depending on its configuration. A typical prompt might be capped at 4,000–8,000 tokens (about 3,000–6,000 words) to balance detail and performance. Longer prompts risk truncation, forcing the system to prioritize recent or critical information, which could reduce answer quality. Developers should check documentation for specific token limits, as exceeding them may result in incomplete processing or errors.
Complexity is less about strict limits and more about how well the model parses multi-layered queries. For instance, a prompt asking to "analyze this code, compare it to Python best practices, and suggest optimizations" is manageable, but adding unrelated tasks (e.g., "also summarize a research paper") might dilute focus. Structuring prompts with clear headers (e.g., "Task 1: Code Analysis") helps the model parse intent. However, overly nested logic (e.g., conditional sub-questions) can confuse output. Testing incremental complexity is key—start simple, then expand.
For optimal results, developers should: (1) Break complex prompts into smaller, focused queries. (2) Use concise language and avoid redundancy. (3) Specify formats (e.g., "Reply in bullet points") to guide output. If hitting token limits, summarize background context or split the prompt into sequential interactions. Tools like token counters can help pre-check input size. Remember, clarity and structure often matter more than raw length or intricacy.