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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteOnce a command-line chatbot works, a useful next step is to make its behavior easier to understand and less fragile. Keep only conversation history that helps the model answer, inspect the response by content-block type, handle expected API errors, and reject blank input before sending a request. These are incremental improvements—not proof that a script is production-ready.
Keep only useful conversation history
A chat request usually includes a list of messages. That list is the context the model can use for the current turn, so decide deliberately which turns belong in it.
If your program begins with a canned assistant greeting such as “Hello,” but that greeting adds no useful context to later answers, you can leave it out of the messages sent to the API. Keep the user and assistant turns that matter for the conversation you want: removing one unnecessary greeting is not the same as discarding all prior turns.
For example, if a user asks a follow-up such as “Can you show me an example?”, the preceding exchange may be needed to understand what “it” refers to. The right history is therefore the minimum context that preserves the behavior your chatbot is meant to support.
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Inspect response blocks instead of assuming plain text
Do not assume the response is a single text string. The SDK response contains structured data, including content blocks that may have different types. Iterate through the blocks and branch on their type before deciding what your program should do with them.
for block in response.content:
if block.type == "text":
print(block.text)
elif block.type == "thinking":
# Inspect only in an appropriate development context.
print("Thinking block received")This illustrates type-aware inspection; check the current SDK documentation for the exact response fields supported by the model and API version you use. Text intended for the user belongs in the application’s normal output. Other block types should not automatically be displayed: use them only for a suitable internal or debugging purpose, and do not treat private reasoning as a general-purpose audit trail.
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Other response fields, such as the model identifier and token-use information, can also help when you are debugging or recording application behavior. They are metadata, not user-facing answer text. Inspect the returned structure before relying on a field or assuming every response includes the same blocks.
Catch API errors at the loop boundary
An uncaught API error can terminate a command-line conversation. Put the API call inside a try/except at the level where your program can give a useful message and decide whether the user can continue. Catch specific SDK exception classes rather than matching the text of an error message.
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try:
response = client.messages.create(
model=model_name,
max_tokens=1024,
messages=messages,
)
except anthropic.APIConnectionError:
print("Could not reach the API. Check your connection and try again.")
except anthropic.APIStatusError as exc:
print(f"The API returned an error (status {exc.status_code}).")
else:
# Process response.content here.
passThe class names in this example reflect the Anthropic Python SDK’s typed-error pattern; confirm the names and attributes against the SDK version installed in your environment. The official Anthropic API error reference documents HTTP error categories including 400 invalid requests, 401 authentication problems, 429 rate limits, 500 internal errors, 504 timeouts, and 529 temporary overload. The SDK’s exception hierarchy and the response details available to your program may vary by version.
Choose recovery based on the failure. A connection problem or temporary service issue may justify telling the user to retry; an authentication problem generally calls for fixing credentials, not repeating the same request. Avoid an automatic retry loop unless it has deliberate limits and handles rate limits appropriately. Error handling can keep a recoverable failure from abruptly ending the session, but it cannot fix an invalid request or unavailable credentials by itself.
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Reject blank input before calling the API
Users can press Enter without typing anything, or enter only spaces. Check the input after trimming whitespace and continue the command-line loop without making a request when nothing remains.
user_input = input("You: ")
if not user_input.strip():
print("Please enter a question.")
continue
messages.append({"role": "user", "content": user_input})Place this check before adding the message to history or calling the API. That keeps an empty turn out of the conversation and gives the user a clear prompt to try again.
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What these changes do—and do not—establish
Intentional history, type-aware response handling, targeted exception handling, and an empty-input guard make a small chatbot’s behavior easier to inspect and maintain. They are practical refinements to a working CLI script, not measured improvements in latency, cost, or reliability, and they do not establish production readiness. A production application needs further design and testing for its own users, failure modes, and operational requirements.
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