Over the past year, our team has witnessed a seismic shift in how we approach AI application development. One of the most exciting innovations we’ve embraced is BAML—a domain-specific language designed specifically for structured prompt engineering. In our journey, BAML has not only simplified our workflow but has also revolutionized the way we create datasets and fine tune our models. In this blog, we share our experiences and insights into how BAML is changing the game for us and could do the same for you.

What is BAML?

BAML, short for “Basically a Made-up Language,” is not just a fancy name. It is a domain-specific language that turns prompt engineering from mere improvisation into a disciplined, efficient coding practice. Instead of generating sloppy strings for LLM interactions, it prescribes that the prompts be regarded as first-class functions with clearly defined inputs and outputs. In this methodical way, every prompt becomes testable, type-safe, and suitable for use with any programming language that we like.

The concept behind BAML is strong and simple when you have prompts working as code and can leverage new development practices with unit testing, version control, and modularity. This has helped us work faster with our AI workflows at a reduced error margin and getting to the market faster eventually.

Benefits That Speak for Themselves

Before BAML, our prompt engineering was a blend of trial and error—a mix of embedding plain text strings within our code and hoping for the best from our language models. The lack of structure often led to unpredictable outputs and cumbersome debugging sessions. With BAML, every prompt is defined with a clear schema, making it easier to validate and test. Here are some of the benefits we’ve experienced:

• Structured Outputs and Type Safety:
  BAML’s type system ensures that outputs adhere to predefined schemas. This eliminates the need for extensive error handling or post-processing corrections, saving us both time and tokens. With structured outputs, we can easily integrate the results into our downstream processes without worrying about inconsistencies.
• Rapid Iteration with Built-in Testing:
  One of the standout features of BAML is its integrated testing framework. We can write unit tests for each prompt before integrating it with our application code. This rigorous testing environment has helped us catch issues early and iterate faster, which is crucial in today’s fast-paced AI development landscape.
• Language Agnosticity:
  BAML isn’t tied to a single programming language. Whether we’re working in Python, TypeScript, or any other language, BAML’s output can be seamlessly integrated into our projects. This flexibility has allowed us to maintain our diverse tech stack without any compromises.
• Enhanced Debugging and Transparency:
  The ability to view the exact prompt that is being sent to the LLM, along with its network request details, has been a game changer. This level of transparency builds trust in the system and makes debugging a straightforward process.

Leveraging BAML for Dataset Creation

Dataset creation is the backbone of any AI project, and here, BAML has been a revelation for us. Traditionally, generating training data for our models involved a lot of manual effort—curating, cleaning, and formatting data into a usable structure. With BAML, we’ve been able to automate significant parts of this process.

Automated Structured Data Extraction

Using BAML, we can now design prompts that not only interact with language models but also extract structured data directly. For instance, we define a schema that outlines the exact format of the dataset we need, and then write a prompt that instructs the LLM to output data accordingly. This approach minimizes errors and ensures consistency across our dataset.

Efficient Data Augmentation

Data augmentation is another area where BAML shines. We can leverage BAML to generate variations of our dataset entries automatically. By defining functions that tweak certain parameters or introduce controlled variations, we have seen a boost in the diversity of our training data. This enriched dataset has contributed significantly to the improved performance of our fine-tuned models.

Real-World Example

In one of our recent projects, we needed to create a dataset for a sentiment analysis model focused on product reviews. Using BAML, we defined a schema for a review that included fields like “reviewText,” “sentiment,” “productName,” and “reviewDate.” We then wrote a series of prompts to parse raw review data from various sources, extract the necessary information, and format it according to our schema. The result was a clean, consistent dataset that dramatically reduced our preprocessing time.

Using BAML for Fine Tuning

Once our dataset is ready, the next critical step is fine tuning our models. Fine tuning involves adjusting the model parameters using our curated dataset to achieve higher accuracy and better performance on specific tasks. BAML’s structured prompt approach is invaluable in this phase.

Streamlining Fine Tuning with Structured Prompts

Fine tuning requires not just data, but data that’s perfectly aligned with the model’s requirements. BAML allows us to create finely tuned prompts that guide the LLMs during the training phase. By ensuring that the training data is in the exact format expected by the model, we minimize the risk of errors during fine tuning and enable the model to learn more effectively.

Improved Token Efficiency and Cost Savings

One of the unexpected benefits we observed is the significant reduction in token usage when using BAML. Because BAML’s prompts are concise and free from unnecessary verbosity, we’re able to cut down on the number of tokens processed. This efficiency translates directly into cost savings, especially when fine tuning large models where token usage can quickly add up.

Enhancing Model Reliability

Fine tuning is as much about refining the model’s capabilities as it is about ensuring reliability. With BAML, every prompt is immediately testable, allowing us to verify that the output meets our standards before it’s used in training. This level of quality control has given us greater confidence in the reliability of our fine-tuned models.

Our Experience and Future Outlook

For us, moving to BAML has been transformational. The philosophy behind the design of the language themselves viewing prompts as first class functions with structured outputs has improved our development process and has also provided us with more opportunities for innovation in dataset production and model refinement.

We have seen how integrated clear schema definitions, integrated testing, and language agnosticity have contributed to the streamlining of the entire AI development lifecycle. With BAML, our team has become more adaptive and agile, workflows have become more predictable, and the models are more robust with higher accuracy.

The advances BAML will make are highly anticipated—especially its first class support to agentic workflows, advanced debugging, and testing capabilities. While integrating BAML into our projects, we look forward to solving more intricate AI problems and augmenting the possibilities of what can be done with AI.

To summarize, if you are in search of optimizing your AI development pipeline with effortless dataset generation, convenient fine tuning, or reliable prompt engineering services, then we are ready to offer transformational results. We built a powerful toolkit around BAML, and would love to apply our results-oriented approach on your projects. 

Let us partner with you to unlock innovative, cost-effective AI solutions that will elevate your organization to new heights.