#049 TAKEAWAYS BAML: The Programming Language That Turns LLMs into Predictable Functions

How AI Is Built

Nicolay here,

I think by now we are done with marveling at the latest benchmark scores of the models. It doesn’t tell us much anymore that the latest generation outscores the previous by a few basis points.

If you don’t know how the LLM performs on your task, you are just duct taping LLMs into your systems.

If your LLM-powered app can’t survive a malformed emoji, you’re shipping liability, not software.

Today, I sat down with Vaibhav (co-founder of Boundary) to dissect BAML—a DSL that treats every LLM call as a typed function.

It’s like swapping duct-taped Python scripts for a purpose-built compiler.

Vaibhav advocates for building first principle based primitives.

One principle stood out: LLMs are just functions; build like that from day 1. Wrap them, test them, and let a human only where it counts.

Once you adopt that frame, reliability patterns fall into place: fallback heuristics, model swaps, classifiers—same playbook we already use for flaky APIs.

We also cover:

• Why JSON constraints are the wrong hammer—and how Schema-Aligned Parsing fixes it
• Whether “durable” should be a first-class keyword (think async/await for crash-safety)
• Shipping multi-language AI pipelines without forcing a Python microservice
• Token-bloat surgery, symbol tuning, and the myth of magic prompts
• How to keep humans sharp when 98 % of agent outputs are already correct

💡 Core Concepts

• Schema-Aligned Parsing (SAP)
• Parse first, panic later. The model can handle Markdown, half-baked YAML, or rogue quotes—SAP puts it into your declared type or raises. No silent corruption.
• Symbol Tuning
• Labels eat up tokens and often don’t help with your accuracy (in some cases they even hurt). Rename PasswordReset to C7, keep the description human-readable.
• Durable Execution
• Durable execution refers to a computing paradigm where program execution state persists despite failures, interruptions, or crashes. It ensures that operations resume exactly where they left off, maintaining progress even when systems go down.
• Prompt Compression
• Every extra token is latency, cost, and entropy. Axe filler words until the prompt reads like assembly. If output degrades, you cut too deep—back off one line.

📶 Connect with Vaibhav:

• LinkedIn
• X / Twitter
• BAML

📶 Connect with Nicolay:

• Newsletter
• LinkedIn
• X / Twitter
• Bluesky
• Website
• My Agency Aisbach (for ai implementations / strategy)

⏱️ Important Moments

• New DSL vs. Python Glue [00:54]
• Why bolting yet another microservice onto your stack is cowardice; BAML compiles instead of copies.
• Three-Nines on Flaky Models [04:27]
• Designing retries, fallbacks, and human overrides when GPT eats dirt 5 % of the time.
• Native Go SDK & OpenAPI Fatigue [06:32]
• Killing thousand-line generated clients; typing go get instead.
• “LLM = Pure Function” Mental Model [15:58]
• Replace mysticism with f(input) → output; unit-test like any other function.
• Tool-Calling as a Switch Statement [18:19]
• Multi-tool orchestration boils down to switch(action) {…}—no cosmic “agent” needed.
• Sneak Peek—durable Keyword [24:49]
• Crash-safe workflows without shoving state into S3 and praying.
• Symbol Tuning Demo [31:35]
• Swapping verbose labels for C0,C1 slashes token cost and bias in one shot.
• Inside SAP Coercion Logic [47:31]
• Int arrays to ints, scalars to lists, bad casts raise—deterministic, no LLM in the loop.
• Frameworks vs. Primitives Rant [52:32]
• Why BAML ships primitives and leaves the “batteries” to you—less magic, more control.

🛠️ Tools & Tech Mentioned

• BAML DSL & Playground
• Temporal • Prefect • DBOS
• outlines • Instructor • LangChain

📚 Recommended Resources

• BAML Docs
• Schema-Aligned Parsing (SAP)

🔮 What's Next

Next week, we will continue going more into getting generative AI into production talking to Paul Iusztin.

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