Use DIFY + LLM to build accurate task processing applications

When building a large -language model (LLM) application, how to ensure the accuracy and repetitiveness of the return result is a common challenge. This article will combine the experience of DIFY + LLM to introduce how to design a precise LLM task processing process to avoid random jitter in traditional LLM output, and achieve accurate task execution through reasonable client logic processing and DIFY arrangement.

1. Understand the randomness of LLM output

In many task scenarios (such as code modification), the output of LLM may have a certain degree of randomness. This is similar to the out-of-order transmission of the UDP protocol. When the LLM model generates output, there is uncertainty in the output due to its "open" interface characteristics. This random jitter can cause difficulties in situations where precise control of the results is required. For example, calling LLM multiple times for the same input text may return different function names, and consistency cannot be guaranteed.

In order to make up for this inaccuracy, an additional control layer can be introduced, similar to the re -transmission mechanism in the TCP protocol, and through hierarchical processing processes to ensure the consistency of the final result.

2. Use DIFY to arrange LLM nodes

DIFY is a stateless service arrangement tool that can encapsulate and arrange LLM nodes, and automatically pass the required context information during the call process. DIFY enables us to simplify the realization of complex logic and perform step -by -step execution of LLM tasks.

The basic idea of ​​using Dify:

• Node design: In DIFY, by setting up different nodes (such asif-else if-elseNode) to select the corresponding LLM node for processing based on the input task type, model name and other parameters.
• Process control: The execution process of each task is passedif-elseThe control flow is distributed, and the model will enter directly after the model processingendNodes, complete the entire process of the task.
• No need to be complicated: DIFY itself is not suitable for complex logic processing, but as a LLM API arrangement tool, depending on the logic of the client to process the required context information.

3. Writing logic on the client

For complex tasks, the client is responsible for providing additional logic processing. DIFY is just a tool for arrange and call LLM nodes. The real context information and business logic should be controlled by the client. Through the client, the following operations can be achieved:

• Dynamically obtain context: In the client environment, transmit the above information according to the real -time data and status to DIFY for LLM nodes.
• Mission status feedback: The client is responsible for managing the status of the task (success or failure) and passingcheckerCheck out the output of the task. If the task does not meet the expected results, the client can trigger the repair mechanism and re -call the LLM node to adjust.

This design method makes LLM more reliable when facing accurate tasks, avoiding the error assumptions of the return result of pure dependency model.

4. Check whether the task is successful and the repair mechanism

In order to achieve accurate task completion of LLM, the client also needs to implement a task verification mechanism. This mechanism usually includes the following steps:

• Return result test: When LLM returns results, the client passescheckerNode checks whether the return value is as expected.checkerResponsible for judging whether the result is successful. If it is unsuccessful, it will trigger the next repair operation.
• repair mechanism: If the task fails, the client will use the pump mechanism (that is, the circular repair mechanism) to repeat the LLM node until the task is successful. Each time you call, you can pass the new context or different prompts to LLM to ensure that the task can gradually approach the correct result.

Typical task repair process:

5. DIFY + LLM design mode

Based on the above discussion, a common design mode when building LLM applications is as follows:

1. input parameters: First of all, DIFY accepts input parameters, such as task type, model name, and reminder.
2. Distribution task:passif-elseNodes, choose the right LLM node according to the task type for processing.
3. Task processing and inspection: After the task is processed, the client passescheckerCheck the results to ensure that the output meets the expectations.
4. Cycle repair: If the task is unsuccessful, the client is repaired through the pump mechanism, and the LLM is adjusted again.

This design model is not only suitable for simple tasks, but also can cope with more complicated scenarios to ensure that LLM achieves higher accuracy when dealing with tasks.

6. Avoid the “exactly once” assumption

There are many assumptions in the computer field, such as the network will not be overtime, etc., and ultimately fails to fully realize it. LLM is similar, it does not guarantee that each output is completely correct. Therefore, we need to consider this in the system design, and the redundant and repair mechanism is adopted to ensure that the final result meets expectations.

Through the reasonable design of the client logic and DIFY arrangement, avoiding LLM as a completely closed solution, but to obtain accurate results by repeated corrections and adjustments.

Summarize

The keys to writing mission-accurate applications using Dify + LLM are:

• Orchestrate LLM nodes through Dify to simplify model calling.
• Handle complex context and logic on the client side to ensure mission success.
• Design task inspection and repair mechanisms to avoid relying on the assumptions of LLM to return one -time accurate results.

This design approach enables LLM to provide reliable output in more complex real-world scenarios.