Example usage

if name == "main": large_prompt = "Write a detailed explanation of asynchronous programming in Python, including examples." * 200 result = safe_claude_call(large_prompt)

if result:
    print("\n--- Claude Output ---\n", result[:300], "...")

Explanation:

- The function retries failed requests up to three times, with exponential backoff on timeouts.
- If the output looks incomplete (truncated), it increases `max_tokens` dynamically.
- If token limits are hit, it shortens the input context and retries gracefully.
- Latency and estimated token counts are printed to aid debugging.

This pattern allows your application to stay responsive and robust under heavy or variable workloads.

Proactive Token Budgeting

Before sending large prompts, it’s best to estimate the total token load. A rough rule of thumb is 4 characters per token. If your input text is too long, truncate or summarize it before sending.

```python
def check_token_budget(prompt, expected_output=1000, limit=200000):
    """Estimate tokens and warn if approaching the limit."""
    input_tokens = len(prompt) // 4
    total = input_tokens + expected_output
    if total > limit:
        print(f"⚠️Warning: Estimated {total} tokens exceeds limit of {limit}.")
        print("Consider chunking input or summarizing content.")
    return total

This simple pre-check prevents you from unintentionally sending massive contexts that could trigger truncation or model rejection.

Chunking Long Inputs

When dealing with very large documents or multi-file codebases, it’s often more efficient to split the input into manageable chunks and process them iteratively. Claude can then summarize or integrate results afterward.

def chunk_text(text, size=8000):
    """Split text into token-sized chunks."""
    for i in range(0, len(text), size):
        yield text[i:i+size]

def summarize_large_text(text):
    """Summarize large content incrementally."""
    summaries = []
    for i, chunk in enumerate(chunk_text(text)):
        print(f"Processing chunk {i+1}")
        summary = safe_claude_call(f"Summarize this section:\n{chunk}")
        if summary:
            summaries.append(summary)
    return safe_claude_call("Combine these summaries:\n" + "\n".join(summaries))

This approach keeps each request within token limits while maintaining coherence in the final output. It’s especially useful when working with logs, transcripts, or code repositories that exceed the model’s context capacity.

Clarification Table: Timeout and Token Handling Strategies

Advanced Tip: Controlled Continuations

Claude can continue truncated responses seamlessly when prompted correctly. Use a follow-up prompt pattern like this:

continuation_prompt = "Continue from the last point, maintaining context and code correctness."
more_output = safe_claude_call(continuation_prompt)
final_output = (result or "") + "\n" + (more_output or "")

This ensures continuity across requests while staying within model constraints.

Timeouts, token limits, and truncation are not signs of failure — they’re natural guardrails that protect both developers and the model from overload. By estimating token budgets, handling retries, and detecting incomplete responses, you can make Claude behave like a reliable component in a distributed system.