I'm working on a modular pipeline using OpenAI's GPT-4 Turbo (via /v1/chat/completions) that involves multi-step prompt chains. Each step has a specific role (e.g., intent detection → function execution → summarization → natural language response). I'm running into architectural questions:

Each step depends on the output of the previous one, and some steps involve function calling function_call: "auto", while others require injecting previous context dynamically.

Questions: Context Transfer:

Is it better to pass raw JSON outputs from prior steps into the system or user prompt?

Does injecting intermediate outputs as tool_calls or appending them to messages yield better alignment?

Function Calling Flow:

In multi-step flows, should I manually parse the function call and immediately invoke the next step, or can I nest that logic inside the tool definition for cleaner chaining?

Is it safe to pass generated outputs back into GPT via system prompts, or does that pollute the context in subtle ways?

Memory Efficiency? What's the best way to trim redundant information between steps without degrading quality?

Is there a limit to how much structured output GPT can “remember” and reuse reliably across chained requests?

    response_1 = openai.ChatCompletion.create(
  model="gpt-4-turbo",
  messages=[{ "role": "system", "content": "You are an intent classifier..." }, 
            { "role": "user", "content": user_input }]
)


response_2 = openai.ChatCompletion.create(
  model="gpt-4-turbo",
  messages=[
    { "role": "system", "content": f"Intent: {intent}" },
    { "role": "user", "content": "Proceed to handle the request using tool if needed." }
  ],
  tools=[...],
  tool_choice="auto"
)

Would appreciate any insights, especially from people building modular AI workflows with OpenAI's API. If you’ve done this at scale, how did you avoid messy context bloating and latency issues?

Thanks!