Welcome to the fourth lesson of Laying the Foundations for Code Translation with Haystack! We're now more than halfway through our journey. So far, you've learned how to preprocess messy inputs and build a translation pipeline that leverages powerful language models. Today, we'll focus on a crucial final step: postprocessing — the art of transforming raw translation outputs into polished, user-friendly results.

By the end of this lesson, you'll know how to validate, format, and explain code translations, making your system's output not just accurate but also clear and helpful. Let's dive in!

After the translation step, the output from the language model is often just a plain string. This raw result can be inconsistent, lack formatting, or be hard to interpret — especially for users who want to understand how the translation was performed.

Postprocessing solves these challenges by:

• Ensuring the translated code is properly formatted and easy to read;
• Providing concise explanations of the translation steps;
• Standardizing the output structure for downstream use.

Think of postprocessing as the final polish: it's what turns a good translation into a great, user-ready result. For example, imagine receiving a translated code snippet with no syntax highlighting or explanation. It's much less helpful than a neatly formatted code block with a brief summary of what changed and why.

To implement postprocessing, we'll create a custom component using Haystack's @component decorator. This component will take the raw translation, format it, and generate an explanation — all in a structured way.

Here's how we start defining our postprocessor:

In this snippet, we define a new class and initialize an LLM that will help us with formatting and explanation. The docstring makes it clear what this component is responsible for. This structure keeps our pipeline modular and easy to maintain.

The heart of our postprocessor is the prompt we send to the LLM. We want the model to return a JSON object containing both the formatted code and a concise explanation. Here's how we build that prompt:

This prompt is carefully crafted to:

• Clearly instruct the LLM on its role and the required output format
• Provide both the original and translated code for context
• Specify that the output must be a JSON object with two fields

By being explicit, we reduce the risk of inconsistent or malformed responses. This is a key skill when working with LLMs: the quality of your prompt directly affects the quality of the output.

Even with a well-designed prompt, LLMs can sometimes return unexpected results. To make our system robust, we need to handle errors gracefully. Here's how we process the LLM's response:

This approach ensures that:

• If the LLM returns valid JSON, we use it directly.
• If there's a parsing error, we still provide the translated code and a helpful explanation, rather than letting the system fail.

This kind of error handling is essential in real-world applications, where unpredictable outputs can and do occur.

With our postprocessor ready, the next step is to connect it to the rest of the pipeline. This ensures that after translation, the output flows directly into our postprocessing component for final formatting and explanation.

Here's how we add and connect the postprocessor:

These connections make sure the postprocessor receives:

• The raw translation from the LLM
• The original code for context
• The source language, which can be useful for explanations

By wiring everything together, we create a seamless flow from input to polished, user-friendly output.

With your postprocessing routine in place, you’ve unlocked the secret to turning raw code translations into polished, user-ready gems. Now, your pipeline doesn’t just translate — it explains, formats, and delivers results that are clear and trustworthy.

Ready to put your new skills to the test? Up next is a hands-on practice section where you’ll build and refine your own postprocessing component. You’ll get to experiment, troubleshoot, and see firsthand how a great postprocessor elevates the entire translation experience.

And there’s more: after you master postprocessing, we’ll tackle one of the most important topics in code translation — adding guardrails to make your system safer and more reliable. You’re almost at the finish line, and your code translator is about to become both powerful and robust!