AI prototypes are easy to build.
A few API calls, a prompt, and you can have something impressive running in an afternoon.
Production AI is different.
Once an AI feature becomes part of a real application, the engineering problems start appearing quickly:
- How do we handle concurrent requests?
- What happens when the model is slow?
- How do we control costs?
- How do we handle provider failures?
- How do we measure response quality?
- How do we prevent one bad request from consuming all available resources?
This is where I found Go particularly interesting.
- Why Go Works Well for AI Backends
AI applications often spend significant time waiting on external systems:
Client
↓
Go API
↓
LLM Provider
↓
Vector Database
↓
External APIs
Go's concurrency model makes it natural to handle many independent operations without creating a complicated threading model.
For example, an AI request might need information from multiple sources:
type Result struct {
Source string
Data any
}
func fetchContext(ctx context.Context) ([]Result, error) {
g, ctx := errgroup.WithContext(ctx)
results := make([]Result, 2)
g.Go(func() error {
data, err := fetchDocuments(ctx)
if err != nil {
return err
}
results[0] = Result{
Source: "documents",
Data: data,
}
return nil
})
g.Go(func() error {
data, err := fetchUserData(ctx)
if err != nil {
return err
}
results[1] = Result{
Source: "user",
Data: data,
}
return nil
})
if err := g.Wait(); err != nil {
return nil, err
}
return results, nil
}
The important part isn't the syntax.
It's the architecture: independent operations can run concurrently while sharing cancellation through the request context.
- Context Cancellation Is Critical
One mistake I've seen in AI integrations is treating model calls like ordinary function calls.
They aren't.
An LLM request can take seconds.
If the user disconnects, there is often no reason to keep spending resources processing the request.
Go's context.Context gives us a clean way to propagate cancellation and deadlines.
ctx, cancel := context.WithTimeout(
context.Background(),
10*time.Second,
)
defer cancel()
response, err := client.Generate(ctx, request)
Timeouts should be intentional.
An AI service without sensible timeouts can turn a temporary provider problem into a resource-exhaustion problem inside your own application.
- Don't Build Your AI System Around One Model Provider
One of the lessons I've learned is to avoid spreading provider-specific logic throughout the application.
Instead of doing this:
Handler
↓
OpenAI-specific code
↓
Business logic
I prefer something closer to:
Handler
↓
AI Service
↓
Model Interface
↓
Provider Adapter
For example:
type Model interface {
Generate(
ctx context.Context,
prompt string,
) (string, error)
}
Now the application depends on an interface rather than a particular provider.
This becomes useful when you need to:
- switch models
- compare providers
- implement fallbacks
- run evaluation experiments
- control costs
- route different workloads to different models
The abstraction isn't valuable because abstractions are inherently good.
It's valuable because model providers and models change quickly.
AI Reliability Requires More Than HTTP Retries
A failed API request is easy to detect.
A successful API request that produces a bad answer is much harder.
That's one of the biggest differences between traditional APIs and AI systems.
For a traditional endpoint:
HTTP 200 + valid response
often means the operation succeeded.
For an AI endpoint:
HTTP 200
doesn't tell you whether the answer was actually useful.
You need another layer of evaluation.
For example:
Request
↓
Model
↓
Response
↓
Validation
↓
Evaluation
↓
Accept / Retry / Fallback
This is where AI engineering starts looking less like ordinary API integration and more like building a probabilistic system.
- Structured Output Makes AI Easier to Operate
Whenever possible, I prefer asking models for structured output rather than parsing arbitrary text.
Instead of:
"The customer appears to be interested in upgrading..."
I'd rather receive something like:
{
"intent": "upgrade",
"confidence": 0.91,
"next_action": "show_upgrade_options"
}
Now the Go application can validate the response before acting on it.
The model generates the information.
The application remains responsible for enforcing the rules.
That's an important boundary.
- Keep Deterministic Logic Outside the Model
LLMs are excellent at tasks involving language and ambiguity.
They're not a replacement for deterministic business rules.
For example, I wouldn't ask an LLM:
"Is this customer allowed to receive a $500 refund?"
if the answer can be determined from application data and explicit business rules.
Instead:
LLM
↓
Extract intent / information
↓
Application
↓
Validate business rules
↓
Execute action
The model can help understand the request.
The application should decide what the system is actually allowed to do.
- Observability Becomes Even More Important
For AI applications, I want to know more than:
POST /generate → 200
Useful telemetry can include:
model
latency
input_tokens
output_tokens
estimated_cost
status
retry_count
tool_calls
evaluation_score
This helps answer questions such as:
Did the new model improve quality?
Why did latency increase?
Which requests are becoming expensive?
Is a prompt change actually improving results?
Without this information, AI development becomes guesswork.
The Most Important Lesson
The biggest lesson I've learned building AI-backed systems is that the model is only one component.
A production AI system looks more like:
┌──────────────┐
│ Client │
└──────┬───────┘
↓
┌──────────────┐
│ Go API │
└──────┬───────┘
↓
┌──────────────┐
│ AI Service │
└──────┬───────┘
┌─────┼─────┐
↓ ↓ ↓
Model Tools Context
│ │ │
└─────┼─────┘
↓
Validation
↓
Business Logic
↓
Observability
The difficult part isn't calling an LLM.
The difficult part is building a system around an LLM that remains "predictable, observable, cost-controlled, and safe to change".
That's where Go has become particularly useful for me: it provides a relatively small, straightforward foundation for building the infrastructure around AI without making the system itself unnecessarily complicated.
And in production AI, simplicity is a feature.
Top comments (2)
The reminder to keep business rules outside the model really resonates. Let the model interpret; let the application enforce what’s allowed. Great practical write-up!
Thanks. I hope we can have a meeting to discuss collaboration.