Post-Training Is How You Keep Your Taste | Fireworks CEO Lin Qiao

Name: Post-Training Is How You Keep Your Taste | Fireworks CEO Lin Qiao
Uploaded: 2026-08-12T12:00:10.000Z
Duration: 28 min 28 s
Channel: Sequoia Capital

Sequoia CapitalAugust 12, 202628m

Topics13

Introduction and Context0:00 The Shift in Software Development0:32 Why Post-Training Matters3:01 Owning Intelligence: The Three Pillars4:00 The Post-Training Progression5:31 Technique Selection Framework8:02 Common Frustrations and Pitfalls9:30 Case Studies and Success Examples14:00 Developer Types and Engagement Models18:32 Market Timing and Strategy20:03 Engagement Models with Fireworks22:02 Reward Signal Best Practices23:30 ROI and Timing Considerations24:32

In a Nutshell

Post-training lets you bake your product’s taste and domain expertise into the model instead of relying on generic APIs. The practical path runs prompting → RAG → supervised fine-tuning → preference tuning → RL, with data quality, systematic evals, and training-serving alignment being the biggest pitfalls. Companies that reach product-market fit should then leverage their user data to create specialized models that are 5-10× cheaper and impossible to clone.

AI-Generated Notes

These notes were generated by AI and may contain inaccuracies.

Fireworks CEO Lin Qiao opens the talk by noting the audience composition: many attendees are engaged in post-training and use Fireworks. The session is structured as 15 minutes on post-training approaches followed by 15 minutes of Q&A.

Fireworks operates as a specialized intelligence platform hosting applications from startups to digital native companies and enterprises. The platform provides visibility into innovation patterns, challenges, and trends among developers.

The past year has seen significant disruption in software and application development. Previously, implementing a good idea required teams of tens of strong product engineers and PMs working multiple quarters. Now, a single person without coding knowledge can accomplish this in a few weeks. This reduction in required resources and timeline is fundamentally changing competitive dynamics in the application space.

Developers are shifting from building on top of off-the-shelf black box APIs toward building deeper moats to create more durable businesses. Recent discussions about open versus closed models reflect the industry's recognition that companies exist to solve unique problems in specialized ways, carrying their own judgment, taste, and conviction into products.

Building on off-the-shelf APIs requires careful consideration of how to maintain special taste, judgment, and unique differentiation. The recommended approach for building durable businesses is to bake judgment, taste, and customer understanding into the intelligence layer rather than relying solely on generic APIs.

Owning intelligence encompasses multiple dimensions:

Data ownership: Intelligence derives from data. Foundation models are built on public and labeled data for common tasks, but each business solves specific problems. The ability to curate high-quality production data and generate synthetic data to enrich production datasets represents one step toward owning intelligence.

Model ownership: After acquiring data, the next step involves converting data into models by building on existing models and owning the weights. Multiple techniques exist to achieve this, each tailored to different problem types and capable of interoperating.

Serving ownership: Once a model effectively solves specific problems, it must be served in production. This includes A/B testing to validate product metric improvements, then iterating through the loop.

Sign in to read the full notes

Get access to AI-generated notes, topic timestamps, and more.

Post-Training Is How You Keep Your Taste | Fireworks CEO Lin Qiao

Introduction and Context00:00

The Shift in Software Development00:32

Why Post-Training Matters03:01

Sign in to read the full notes