Adapting Foundation Models for Specific Applications
Adapting a foundation model for a real application is partly a practical skill, and practical skills are shaped by the conditions you have worked under.
Can Experience at One Scale Carry Over?
Adapting a foundation model for a real application is partly a practical skill, and practical skills are shaped by the conditions you have worked under. A common problem in AI research and development is that the knowledge needed to work at one scale does not automatically transfer to a much larger one. This article uses that problem as its starting point and then follows the parts of an AI application to see how they connect.
What do you think happens?
A team has run its training and serving work on 10 GPUs for two years. Does that mean the team can run the same work on 1,000 GPUs?
Reveal answer
No, experience at one scale does not guarantee ability at a much larger scale.
The source presents scaling as a challenge in its own right. A team's competence is tied to the scale it has actually operated at, so 10 GPUs of experience is not the same as 1,000 GPUs of experience.
Why Scale Changes the Problem
The source describes teams that struggle to move from 10 GPUs to 1,000 GPUs. It also notes that such teams are offered very generous compensation packages, which shows how scarce this skill is. The important idea is the one the source states directly: a team's competence is tied to the scale it has actually operated at. A team that is excellent at one scale has demonstrated that skill only at that scale.
Where the Model Sits in the Stack
The source gives a compact view of the field: AI engineering is just software engineering with AI models thrown in the stack. The discipline does not replace software engineering. The skills of building, testing, and maintaining applications still apply, and the AI model is added as one more part of the system. A useful way to picture this is as a set of layers, with the model as one layer among others rather than a separate world.
Tracing One Feature Through the Stack
The next idea is about how the parts of a working application relate. The example below follows one feature from a user request down to the model and back. The scenario is an invented illustration, not taken from the source, but it shows the flow that the stack view above describes.
A summarize button in a web app
A user clicks a Summarize button on a web page that contains a long article. Trace what happens from the click until the summary appears on the screen.
- 1Step 1: the request leaves the interface
The user clicks the button. The web interface packages the article text and the request into a message and sends it to the application logic.
- 2Step 2: the application logic prepares the work
The application checks the input, prepares the prompt for the model, and decides what to do with the result. This is ordinary software engineering work: validating input and handling the flow.
- 3Step 3: the model produces output
The application sends the prepared input to the AI model. The model is one component among the others, and the application depends on it to produce the summary text.
- 4Step 4: the result travels back
The output returns through the application logic, which may format or check it, and then back to the interface, which displays the summary to the user.
The feature is a chain of layers, and the AI model is only one link. Building and maintaining the application still depends on the same practices as any other software system.
Why the Vocabulary Is Imprecise
Practitioners share a vocabulary, and the source argues that this vocabulary is not always precise. It says the AI research community is strong in many areas, but naming is not one of them. Two examples are given. The first is the pair 'pre-training' and 'post-training', which the source describes as lacking imagination. The second is 'large language models', which the source says is hardly a scientific term because of the ambiguity of the word 'large'.
| Term | Why the source calls it imprecise | What to check in context |
|---|---|---|
| large language models | The word 'large' is ambiguous, so the term is hardly a scientific term | Ask what size is meant and what the speaker counts as large |
| pre-training | Named in a way the source considers unimaginative | Ask which stage of model development the speaker means |
| post-training | Named in a way the source considers unimaginative, and paired with pre-training | Ask what work happens after the earlier stage in this specific context |
Terms the source says can be imprecise. The source gives no precise definitions for them, so the meaning must be checked in context.
Tools for Building AI Web Applications
When an AI model is wrapped in a web application, developers often reach for a dedicated framework. The source names three common tools for building AI web apps: Streamlit, Gradio, and Plotly Dash. Those names are the full extent of what the source tells you about them. It does not compare them, describe their features, or say which to choose.
| Tool | What the source states | What the source does not state |
|---|---|---|
| Streamlit | Named as a common tool for building AI web apps | Features, strengths, trade-offs, or how it compares |
| Gradio | Named as a common tool for building AI web apps | Features, strengths, trade-offs, or how it compares |
| Plotly Dash | Named as a common tool for building AI web apps | Features, strengths, trade-offs, or how it compares |
The source names these tools without describing them.
Treat a framework name as a starting point for your own research. Read each tool's documentation and test it against your application's needs before committing to it.
Mistakes Beginners Make With Scale and Terms
Common mistake Assuming that success at 10 GPUs means success at 1,000 GPUs
What it looks likeA team that runs its work smoothly on a small cluster expects the same process to work unchanged on a cluster a hundred times larger.
Why it failsThe source states that experience at one scale does not guarantee the ability to work at a much larger scale. A team's competence is tied to the scale it has operated at.
Do this insteadTreat the move to a larger scale as a new challenge and plan for it, rather than assuming earlier experience transfers.
Common mistake Inventing the technical reasons for the scaling gap
What it looks likeStating a specific cause for why large clusters are harder to run, without any support.
Why it failsThe source does not list the specific technical reasons for the gap, so any cause given would be invented.
Do this insteadSay what the source establishes, namely the scale dependence of competence, and look up specific causes in dedicated material.
Common mistake Taking imprecise AI terms at face value
What it looks likeAssuming that 'large language models' has one fixed, agreed meaning in every conversation.
Why it failsThe source argues that these terms can be imprecise, and the word 'large' is ambiguous.
Do this insteadAsk what the speaker means in context before relying on the term.
Common mistake Treating AI engineering as a separate discipline that replaces software engineering
Why it failsThe source says AI engineering is software engineering with AI models integrated into the stack, not a separate discipline.
Do this insteadApply the building, testing, and maintenance practices you already know, and add the model as one more part of the system.
Check Your Understanding
A colleague says: 'Our team has shipped three AI features on a small cluster, so we are ready to run the same features on a cluster of 1,000 GPUs next month.' Using what the source states, explain what is and is not justified by this claim.
Hints
- Look at the source's core point about competence being tied to the scale a team has operated at.
- Consider whether the source tells you the specific technical problems of a larger cluster.
- Separate what the experience proves from what it leaves unproven.
Key Points to Keep
- 1Experience at one scale, such as 10 GPUs, does not guarantee the ability to work at a much larger scale, such as 1,000 GPUs.
- 2AI engineering is software engineering with AI models integrated into the stack, not a separate discipline.
- 3Terms like 'large language models', 'pre-training', and 'post-training' can be imprecise, so check their meaning in context.
- 4Streamlit, Gradio, and Plotly Dash are common tools for building AI web apps, but the source does not describe or compare them.
Key Takeaways
- Experience at one scale, such as 10 GPUs, does not guarantee the ability to work at a much larger scale, such as 1,000 GPUs.
- AI engineering is software engineering with AI models integrated into the stack, so existing software practices still apply.
- The terms 'large language models', 'pre-training', and 'post-training' can be imprecise, so check what they mean in context.
- Streamlit, Gradio, and Plotly Dash are common AI web app tools, but the source names them without comparing or describing them.