This opening slide establishes the central theme of this section.
While large language models have dramatically expanded what software can do, they do not form complete
applications on their own.
As highlighted on page one, the real challenge lies in integrating Llms into production systems that
are reliable, scalable, and maintainable.
This section focuses on back end architecture because that is where most LLM applications succeed or
fail.
A powerful model cannot compensate for poor system design without proper APIs request handling, error
recovery, and observability.
Even the best models produce fragile systems.
The visual of server infrastructure reinforces an important idea llms live inside distributed systems.
They must coexist with databases, caches, APIs, and external services.
Throughout this section, we will focus on the engineering patterns and architectural discipline that
separate robust applications from experimental prototypes.
By the end of this section, you should think of lrms not as magic components, but as dependencies
that must be carefully orchestrated within a well-designed back end.
This slide explains why back end architecture is a first order concern for LM applications.
As stated on page two, LMS are powerful inference engines, but they are not applications by themselves.
Production systems require the same rigor as any distributed system.
Authentication, authorization, scaling, observability, and fault tolerance for back end design leads
to cascading failures.
High latency degrades user experience.
Uncontrolled API usage causes runaway costs.
Inconsistent behavior erodes trust.
Importantly, these are not model problems, they are architectural problems.
The slide emphasizes a critical insight LMS are dependencies in your system.
Just like databases or external APIs, how you integrate them determines whether your application succeeds
or fails.
The listed production requirements and common failure modes reinforce that back end concerns, not prompt
quality.
Dominate real world reliability.
This mindset shift is essential for engineers transitioning from experimentation to production grade
systems.
This slide presents a reference architecture for production LM applications as shown on page three.
A typical stack includes several interconnected components working together at the edge.
A client application, web, mobile, or CLI initiates requests.
The API layer, commonly built with fast API handles validation, routing, and authentication.
LM services perform inference and orchestrate tool calls.
Supporting layers include vector databases for retrieval.
Augmented generation reads for caching and PostgreSQL for application state and user data.
The diagram clearly illustrates a layered design and the key architectural goal is emphasized loose
coupling with strong boundaries.
Each component should be independently deployable, testable, and replaceable.
This modularity is what allows teams to swap Lem providers, upgrade vector databases, or refactor
business logic without rewriting the entire system.
Good architecture creates flexibility, not lock in.
This slide explains why fast API has become the framework of choice for Lem powered backends.
As described on page for fast API combines Python's ecosystem advantages with performance comparable
to Node.js and Go.
Native async support is critical.
Lem API calls can take seconds, and blocking the event loop would cripple throughput fast APIs.
Async first design allows systems to remain responsive even under heavy load.
Type safety through pedantic catches.
Errors early preventing malformed requests from reaching downstream services.
Auto generated open API documentation keeps front end and back end teams aligned.
Streaming support enables server sent events, allowing users to see tokens as they are generated.
The rule at the bottom of the slide is decisive.
Async first architecture is mandatory for LM applications.
Synchronous code may work in prototypes, but it becomes a bottleneck in production.
This slide reinforces that framework choice is not cosmetic, it directly impacts scalability and reliability.
This slide focuses on API design as the contract between clients and your LM infrastructure.
Page five outlines common endpoint patterns such as chat for conversational interfaces, slash completion
for stateless generation, slash embed for vector creation, and slash tools for function execution.
Each endpoint should have a single, well-defined responsibility.
This clarity simplifies testing, debugging, and scaling.
The slide highlights three core design principles fin controllers, logic, isolation, and explicit
schemas.
Route handlers should delegate business logic to service layers rather than embedding it in HTTP code.
LM orchestration should remain separate from transport concerns.
Pedantic schemas should define all requests and response payloads to enforce structure.
The best practice callout is key.
Treat lm API calls like any external service.
Wrap them in abstraction layers so they can be mocked, retried, monitored, and replaced without disrupting
your application.
This slide walks through the complete request lifecycle for an LM application, as shown on page six.
Every request flows through multiple stages, each introducing latency considerations and failure risks.
The flow begins with a client request followed by input validation using pydantic schemas.
Context retrieval pulls relevant information from vector databases in Rag pipelines.
The formatted prompt is then sent to the LM provider.
Optional tool execution may follow depending on model output.
Finally, responses are post-processed validated and returned to the client.
The slide emphasizes that this flow must be deterministic, observable, and resilient.
Each stage should emit metrics and logs.
Distributed tracing tools like Opentelemetry help visualize where time is spent and where failures occur.
A disciplined request life cycle is what makes debugging possible and performance optimizable.
Without it, LM systems become opaque and fragile.
This slide addresses one of the most challenging aspects of LMS systems latency.
As explained on page seven.
LM response times are inherently variable, ranging from milliseconds to tens of seconds.
Traditional blocking request response patterns are inadequate.
Streaming responses using server sent events dramatically improve perceived performance by delivering
tokens incrementally.
Aggressive timeouts, typically 30 to 60s prevent requests from hanging indefinitely.
The slide also introduces partial responses and async execution.
Returning partial output with clear error indicators is often better than failing completely.
For complex workflows, long running tasks should be offloaded to background workers.
The rule at the bottom is clear and uncompromising.
Never block the request thread waiting for LM responses.
Async execution is essential for system responsiveness and scalability.
This slide explains why error handling is a defining feature of production ready LM systems.
LM APIs fail in many ways.
Timeouts, rate limits, malformed responses, content policy violations, and provider outages.
Page outlines best practices for handling these failures.
Structured errors with clear codes make debugging easier.
Graceful degradation allows systems to fall back to cached or simpler responses.
Clear client messaging prevents confusion.
Circuit breakers stop repeated calls to failing services.
Retries should be applied selectively.
Transient errors like network timeouts and 503 responses are valid retry candidates.
Exponential backoff prevents thundering herd effects.
Idempotency keys avoid duplicate operations when retrying state changing requests.
The warning at the bottom is critical.
Retries without limits lead to cost explosion.
Retry logic must always be bounded and carefully controlled.
The final slide synthesizes the core lessons of this section.
Back end architecture determines whether an LLM application scales, performs reliably, and stays within
budget.
It must be treated with the same rigor as any distributed system.
Fast API enables the async first design required for streaming, concurrency and non-blocking I o.
A disciplined request lifecycle ensures every interaction flows through validation, context retrieval,
inference, and post-processing in a predictable and observable way.
Error handling is not an afterthought.
It is a core feature.
Structured exceptions retry with backoff.
Graceful degradation and clear messaging are essential for resilience.
The closing statement captures the philosophy of this section.
Great LLM applications are built like great software, not demos.
Architectural maturity, not model size is what separates prototypes from production systems.