Southeast Asian fresh food e-commerce companies use Claude for order quality inspection, saving 3 customer service positions, but they've encountered 2 fatal pitfalls.

Last week, our team dealt with an unexpected incident: On the day of the major promotional event, 18% of the after-sales tickets got stuck in the pending queue due to an error in the output format caused by Claude. As a result, the compensation for damaged fresh fruits requested by customers was processed only after a delay of 4 hours, and the return rate for that day increased by 2 percentage points.

Let me explain clearly for those who haven’t come across it before: What exactly is Claude?

It is a large language model developed by Anthropic. The latest version, 3.5 Sonnet, has a single-context window that can handle up to 200,000 tokens, which is approximately equivalent to 150,000 Chinese characters. The reason we initially chose it was because it allowed us to input the entire order flow, the OCR results of user-provided evidence images, and the platform's compensation rules all at once, without having to split the data into multiple requests.

The three actual benefits we obtained from using it

The most immediate benefit is the reduction in labor costs: instead of having 6 customer service staff members responsible for order quality inspection, only 3 are needed now to handle any exceptional cases. This results in a monthly savings of $4,200 in labor expenses alone.

The second improvement is the increased processing speed: previously, it took an average of 2 minutes for a manual review of a request, but now most requests are completed within 15 milliseconds. Users can receive results within about 5 seconds after submitting their compensation claims, and we have seen a direct 17% increase in user satisfaction in our backend data.

The third point is that the execution of rules has become more consistent: Previously, manual reviews often led to the same type of issues, where some people received full compensation while others only received 30%. There were dozens of complaints each month about unfair rulings. After implementing Claude to uniformly apply the rules, the number of such complaints has dropped to less than three per month.

Don't just look at the benefits; we almost had to shut down the service due to these two problems.

The first issue was related to rate limiting: We initially connected directly to the official API without implementing any layers of redundancy. On the day of the major promotion, the number of requests increased by three times, and the official API immediately returned a 429 error code. Since we didn't have a manual fallback mechanism in place, thousands of service requests got stuck. Later, we added a smaller, less resource-intensive model as a backup solution. If three consecutive requests failed, the system would automatically switch to the smaller model; only if that also failed would it resort to manual intervention. Since then, we have never experienced any more cases of a large number of requests getting stuck simultaneously.

The second issue is the instability of the structured output: Initially, we asked it to return the penalty results in JSON format, but in about 2% of cases, it would add a bunch of explanatory text outside the JSON, causing our parsing script to crash directly. Later, we added a sentence at the end of the prompt that said, “If your output is not pure JSON, you will be shut down,” and the incidence of this problem dropped to less than 0.1%.

Who is suitable for using it? And who should absolutely not touch it?

If your team frequently deals with a large amount of repetitive tasks with clear rules and significant amounts of text, such as reviewing e-commerce orders, categorizing customer service tickets, or initially reviewing contract terms, and is willing to spend 1-2 weeks adjusting the prompts and implementing a degradation mechanism, then Claude can help you save a lot of money and time.

If your scenario requires 100% accuracy in the results, such as in medical diagnoses, final reviews of financial transactions, or if your team doesn't even have dedicated operations or development personnel and you want to use the system without any debugging, then don't even consider it – the chances of encountering problems are much higher than you might think.

Two practical tips for first-time users

First of all, don’t go straight to production mode when you start. Run the system in shadow mode for 7 days first: all requests will be processed by both human operators and the Claude algorithm simultaneously. Compare the consistency of the results from both methods. Only once the consistency reaches over 95%, switch the traffic to the production mode. In our case, we ran the shadow mode for 10 days and identified 3 discrepancies in the way the rules were interpreted. We corrected the prompts in advance, which prevented any issues from occurring.

Secondly, don't overwhelm the highest-performance version with all requests; use Haiku whenever it's appropriate. We later transferred simple ticket classification requests to Haiku, which directly reduced the token cost by 60% and doubled the speed, with no difference in performance at all.

Frequently Asked Questions

• Will there be any issues with data leakage?If your data is sensitive, it is recommended to purchase the enterprise version directly and sign a data processing agreement. Anthropic will not use the data from enterprise version requests to train its models. We have been using the enterprise version for 8 months without any data-related issues.
• Which one is better than GPT?If your scenario requires processing of long texts and a high level of context understanding, choose Claude; if you need multimodal generation, such as drawing, choose GPT. We are using both right now, each for different scenarios.