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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsReliable LLM applications are built by engineering around a model’s strengths and limitations—not by assuming the model will behave like deterministic software. Start with a bounded task and measurable success criteria, test candidate models on representative inputs, and establish an evaluation baseline before optimizing. Then add only the techniques the task needs: clear prompting, retrieval, tools, or, when evidence supports it, fine-tuning.
What LLM development involves
For most teams, LLM development means building an application that uses an existing model, not training a foundation model from scratch. The work includes defining the task, connecting the model to the right data and services, evaluating its outputs, and operating the whole system safely and consistently.
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A model can produce a useful answer on one run and a different answer on another. Its behavior may also change between model versions. Treat prompts, model configuration, retrieval, application logic, and evaluation as parts of one system; a promising demo alone does not establish production reliability.
1. Define the task and its boundaries
Before choosing a model, write down who will use the application, what they need it to do, what information it will receive, and what a good result looks like. AWS’s generative AI lifecycle guidance recommends scoping goals, requirements, risks, data needs, and success measures. Google Cloud also cautions that poor or incomplete input data can lead to poor output.
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- Use scikit-learn to track an example ML project end to end
- Explore several models, including support vector machines, decision trees, random forests, and ensemble methods
- Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection
- Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers
- Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning
- Specify the task: Describe the work in concrete terms, such as answering questions about approved documents or drafting a response for a person to review.
- Identify the source of truth: Decide which documents, systems, or user-provided facts the answer should rely on, and whether that information changes over time.
- Define acceptable behavior: Include cases where the application should answer, ask for clarification, refuse, or hand the work to a person.
- Set measurable criteria: Choose checks for usefulness and correctness, plus any limits for response time, cost, privacy, or risk that matter to the product.
- Assess whether an LLM is needed: If ordinary code, search, or a fixed workflow solves the task more simply, use that instead.
Keep the first version narrow enough to test. If a wrong answer could cause material harm, design human review or approval into the workflow rather than treating it as an optional safeguard.
2. Choose a model and hosting approach by testing
Compare candidate models on the application’s actual work, not on a general impression of capability. Run the same representative examples through each candidate and evaluate task quality alongside latency, cost, required modality and features, context needs, and provider or hosting constraints. AWS’s model-selection guidance also identifies training data, context window, availability, pricing, and infrastructure compatibility as factors to consider.
| Decision area | What to check |
|---|---|
| Task quality | Does the model produce correct, useful results on representative inputs and handle the application’s edge cases? |
| Capabilities | Does it support the required input and output modalities, context length, tool use, and other necessary features? |
| Latency and throughput | Does response time and capacity meet the application’s expected traffic and user experience requirements? |
| Cost | Measure usage or serving costs against successful, useful tasks—not simply the number of requests. |
| Control and operations | Can the provider or hosting setup meet data-handling, security, integration, and operational requirements? |
| Evaluation and safety | Can the team observe failures, test edge cases, and apply human review where needed? |
A larger or more capable model may bring higher cost or latency; those trade-offs should be measured on the workload rather than assumed. Google Cloud’s “Develop a generative AI application” guide puts the selection principle simply: “Choose the most affordable model that still meets your response quality and latency requirements.”
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Managed endpoint or self-managed serving?
A managed endpoint can reduce the infrastructure work your team must handle. Self-managed serving may offer finer control but leaves your team responsible for more of the serving environment. Compare both against actual operating, security, integration, scale, and control requirements, and forecast traffic and budget before settling on a deployment shape.
3. Build a small working application
Start with an explicit prompt that states the task, relevant instructions, and the context the model needs. Include examples when they clarify the expected response. Connect the application to the model API and only the data or services required for the defined task.
If the application needs current information or must take an action, integrate an appropriate tool or function. A model suggesting an action is not the same as an application safely executing it: validate inputs, enforce permissions in application code, and handle credentials securely. Google Cloud distinguishes function calling from extensions; when an integration requires credentials in code, treat that as a security responsibility, not as something made safe by the model call itself.
When retrieval-augmented generation fits
Use retrieval-augmented generation (RAG) when an answer needs to draw on external information that may be large, private, or updated more often than the model itself. The application searches a data source, selects relevant material, and adds it to the model’s context so the model can formulate a response grounded in that material. Embeddings and a vector database are common implementation components, but they do not guarantee that retrieval is accurate.
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Evaluate the retrieval pipeline as well as the generated answer. Relevant concerns include whether the right passages are found, whether source content is fresh, how documents are split into chunks, and whether access controls prevent users from retrieving material they should not see. A fluent response cannot compensate for missing, stale, irrelevant, or unauthorized context.
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4. Choose prompting, RAG, tools, or fine-tuning for the diagnosed problem
These techniques solve different problems; selecting one should follow an observed need rather than a preference for a particular method.
| Technique | Use it when | What to evaluate |
|---|---|---|
| Prompting | The model needs clearer instructions, output constraints, or context already available to the application. | Whether the revised instructions improve the target behavior without harming other tested cases. |
| RAG | Answers need to use relevant information from an external or changing source. | Retrieval relevance, source freshness, access control, and answer grounding. |
| Tools or function calling | The application needs to access a capability or perform an action through an integrated service. | Correct tool selection and arguments, permission checks, error handling, and safe execution. |
| Fine-tuning | A specific behavior remains inadequate after diagnosing prompt, context, retrieval, model capability, application logic, and task-definition issues. | Dataset suitability and quality, the target behavior, and results on held-out representative evaluations. |
Do not reach for fine-tuning just because some answers are wrong. First find out whether the prompt is unclear, context is missing, retrieval failed, application logic is faulty, or the model lacks the capability the task demands. Fine-tuning requires a suitable dataset and method, and the resulting model still needs evaluation.
Google Cloud describes supervised tuning, reinforcement learning from human feedback (RLHF), and distillation as options whose suitability depends on the model and objective. Availability is provider- and model-specific. OpenAI’s currently retrieved model-optimization documentation says its fine-tuning platform is being wound down and is no longer accessible to 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. Those terms can change, so check the current provider documentation before planning around a particular fine-tuning service.
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Create a set of representative inputs and expected outputs or grading criteria before trying to improve the application. Include ordinary cases as well as incomplete inputs, edge cases, and adversarial attempts relevant to the task. Record the baseline so later changes can be compared with it.
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OpenAI’s model-optimization guidance describes an iterative loop: write evaluations, prompt with relevant context, consider fine-tuning for suitable use cases, test on representative data, refine prompts or training data, and repeat. Because outputs are non-deterministic and behavior can change across model snapshots and families, a result from one run or one version should not be treated as a permanent guarantee.
Combine automated checks with human review
Automated evaluations make repeated checks easier to scale, but natural-language quality includes context and nuance that a metric can miss. Google Cloud recommends human evaluation alongside metrics and warns that metrics can oversimplify quality. Use human review for samples and cases where correctness, appropriateness, or unsupported claims are difficult to score automatically.
- Check factual correctness and whether the response follows the task’s requirements.
- Test whether the application asks for clarification, refuses, or hands off when it should.
- Look for unsupported claims, including answers that sound confident despite missing evidence.
- Track quality together with latency and cost so an optimization in one area does not quietly break a requirement in another.
After changing a prompt, model, or retrieval setup, rerun the evaluation set. Add carefully reviewed real-world examples over time so the tests reflect the failures and use cases the application actually encounters.
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6. Prepare the application for production
Move from a prototype to a controlled release by treating the prompt, model identifier and configuration, application code, dependencies, and evaluation data as coordinated release artifacts. AWS recommends promoting validated prompts and model versions with their associated settings, and carrying evaluation datasets into later stages of the lifecycle.
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- Validate integration: Test the complete path through the application, model, data sources, and tools, including timeout and error handling.
- Review security and privacy: Check data handling, credential protection, user permissions, and exposure of sensitive inputs and outputs.
- Test scale and failure behavior: Confirm the deployment can handle expected traffic and define what happens when a model, retrieval source, or tool is unavailable.
- Plan release and rollback: Version the artifacts and infrastructure, use a controlled rollout, and retain a known-good configuration to restore if needed.
- Keep evaluation assets with the release: Preserve the dataset and results used to approve a version so changes can be checked against the same standard.
A proof of concept is a place for substantial prompt and model experimentation. Once a candidate is validated, preproduction work should focus on infrastructure and deployment tuning rather than assuming the experimental setup is ready for live use.
7. Monitor behavior after launch
Production quality can drift as users, source data, prompts, or model versions change. Monitor both generated outputs and operating behavior, then feed meaningful failures back into the evaluation set and development cycle. AWS gives accuracy, toxicity, and coherence as examples of output measures to monitor; choose measures that fit the actual task rather than treating any one metric as a complete quality score.
Use user feedback and reviewed production examples to identify where the system fails, but avoid treating unreviewed outputs as ground truth. When requirements or source information change, update the application and its evaluations deliberately so the release continues to meet its defined success criteria.
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