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Reliable LLM development is mostly application engineering: define a bounded task, choose a model that performs well on representative cases, build the right prompt, data access and tools around it, and evaluate the whole system before and after launch. Most teams do not need to train a foundation model from scratch. They need a repeatable way to detect failures, control risk and improve the application without losing sight of quality, latency and cost.
Start by defining the job
Before selecting a model or framework, write down what the application is supposed to do and where its authority ends. A clear scope gives you something concrete to build and test; a vague goal such as “add AI to support” does not.
- User and task: Who will use the application, and what specific work should it help them complete?
- Inputs and output: What information will the system receive, and what form should its response take?
- Source of truth: Which data or service should determine the answer, and how current must it be?
- Failure cost: What happens if the system is wrong, incomplete or overconfident? Decide which outcomes require refusal, clarification or human review.
- Success measures: Define how you will judge useful, correct behavior alongside acceptable response time and operating cost.
AWS’s guidance on generative AI development recommends scoping goals, requirements, risks, data needs and success measures. Google Cloud also cautions that poor or incomplete input data can produce poor output. These are practical reasons to keep the first version narrow and test whether generative AI improves on ordinary code, search or an existing workflow before committing to it.
Choose a model and hosting approach by testing
Do not choose by model size, reputation or a single public benchmark. Use representative examples from your actual task and compare candidate models against the requirements you established. Google Cloud recommends choosing the most affordable model that still meets response-quality and latency needs; AWS also identifies factors such as context window, training data, pricing, availability and infrastructure compatibility.
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| Decision area | What to compare |
|---|---|
| Task quality | Correctness and usefulness on the application’s real inputs, including edge cases. |
| Modality and capabilities | Required text, image, audio or video handling; tool use; context length; and any tuning needs. |
| Latency and throughput | Response time for users and capacity under expected traffic. |
| Cost | Model usage or serving infrastructure relative to successful, useful tasks. |
| Control and operations | Data handling, security, integration, hosting control and the operational work your team must own. |
| Evaluation and safety | Behavior on difficult cases, need for human review and ability to observe failures. |
Run the same evaluation set against every candidate. A larger model may improve quality for a task, but can also increase latency or cost; that trade-off must be measured in your workload rather than assumed. The same applies to managed versus self-managed hosting: managed deployment can reduce infrastructure work, while self-managed serving offers more control but leaves resource management to your team. Forecast demand and test the deployment shape against the latency and scale your application needs.
Build the application around the model
A first working version needs a clear prompt, application code that calls the model, and any relevant data or services. A prompt should state the task, provide relevant instructions and context, and include examples when they help establish the expected behavior. Keep model interaction inside the application boundary so you can test and change it as part of the system.
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Use prompting for instructions and context
Prompts are the natural first tool for clarifying the job, specifying response requirements and supplying context. They cannot make unavailable facts current or guarantee a response is correct. If the model lacks the information it needs, diagnose that as a context or data problem rather than endlessly rewriting instructions.
Use retrieval when answers depend on external information
Retrieval-augmented generation (RAG) is useful when answers need to draw on external or changing information. The application searches a data source, then adds relevant retrieved material to the model’s context. Embeddings and a vector database are common components, but they are not the whole solution: retrieval quality, source freshness, chunking and access control all affect the answer. Evaluate whether the right material is found and whether the answer uses it appropriately.
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Use tools when the application must act or query live systems
Function calling or other tool integrations let an application access capabilities or perform actions beyond generating text. For example, the application can obtain live information from a service rather than relying on model memory. Treat a proposed tool call as an input to application logic, not as authorization: validate arguments, enforce permissions and handle credentials securely. Google Cloud distinguishes function calling from extensions; its documentation notes that extensions can require credentials in code.
Choose RAG, fine-tuning or another fix by diagnosing the failure
RAG and fine-tuning solve different problems. RAG supplies relevant information at answer time; fine-tuning changes a model’s learned behavior through training. Neither should be added just because the application uses an LLM.
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| Observed need or failure | First approach to consider | What to verify |
|---|---|---|
| The answer needs current, private or domain-specific source material. | RAG or a direct query to the source system. | Whether retrieval finds the right, permitted and up-to-date information. |
| The model needs clearer instructions or a better-defined response format. | Prompting and application-level validation. | Whether the revised prompt improves representative cases without harming others. |
| The application must retrieve live data or carry out an action. | Tool or function integration. | Whether the application safely validates and authorizes each action. |
| Behavior remains unsuitable after requirements, prompt, context, retrieval and application logic have been checked. | Evaluate whether fine-tuning is appropriate. | Whether a suitable dataset and method exist, and whether measured gains justify the added work. |
When an output fails, identify the cause before changing techniques. The issue may be unclear requirements, missing context, a retrieval failure, model capability or application logic. Fine-tuning is not a substitute for fixing these problems. Google Cloud describes supervised tuning, reinforcement learning from human feedback (RLHF) tuning and distillation as options whose suitability depends on the model and objective; any tuning result still needs evaluation.
Provider availability can also affect this choice. OpenAI’s model optimization documentation has reported that its fine-tuning platform is being wound down, with access limited for new users while existing users can create jobs for a limited period; it also says fine-tuned models remain available for inference until their base models are deprecated. This is a provider-specific, time-sensitive statement, not a general rule about fine-tuning. Check the current provider documentation before designing around a tuning workflow.
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Establish evaluations before optimizing
LLM outputs are non-deterministic, and behavior can vary across model snapshots and model families. A successful demo is not evidence that a change is safe across the application’s full workload. Build an evaluation baseline early and rerun it whenever you change prompts, models, retrieval or other behavior that could affect responses.
- Collect representative inputs. Include ordinary requests as well as incomplete, ambiguous, adversarial and other difficult cases that matter to the task.
- Write expected behavior or grading criteria. For some cases, define a suitable answer; for others, specify requirements such as when to ask for clarification, refuse or escalate.
- Run a baseline. Record how the current system performs before optimizing so that changes can be compared against an established result.
- Combine automated checks with human review. Automated metrics make repeated checks practical, but natural-language metrics can oversimplify quality. Human review can catch context and nuance that a metric misses.
- Track trade-offs. Assess quality alongside latency and cost so an apparent quality improvement does not undermine another requirement.
- Repeat after changes. Rerun the evaluation when the prompt, model, retrieval setup or application changes, and add controlled real-world cases as they become available.
OpenAI’s optimization guide describes an iterative cycle of writing evaluations, prompting with relevant context, considering fine-tuning for some use cases, testing on representative data and refining prompts or training data. The key is the loop: each change should be judged against the task’s evidence, not a handful of favorable examples.
Prepare the system for production
Production quality depends on the surrounding system, not only on the model’s generated text. Treat the prompt, model identifier and configuration, application code, dependencies and evaluation dataset as coordinated release artifacts. AWS recommends promoting validated prompts and model versions with their associated settings, carrying evaluation datasets forward, and using the preproduction stage to focus on infrastructure and deployment tuning after significant experimentation in proof of concept.
Before rollout, verify the integration and the operational behavior of the application. Include checks for security and privacy requirements, scale, failure handling and rollback. Use controlled deployment and versioned infrastructure so a release can be traced and, if needed, reversed.
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- Failure handling: Decide how the application responds when a model or connected service fails, returns unusable output or cannot complete a request.
- Security and privacy: Check how data is handled by the model and connected systems, and enforce access controls for retrieved information and tools.
- Monitoring: Watch operating behavior and output quality after launch. AWS gives accuracy, toxicity and coherence as examples of generated-output measures to monitor.
- Feedback loop: Use observed failures and user feedback to improve the evaluation set and application as requirements or source data change.
A launch is the start of operating the application, not the end of evaluation. Monitoring helps reveal behavior that a prelaunch test set did not cover; controlled updates let you incorporate those findings without losing track of what changed.
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