Generative AI and AI Agents: A Practical Guide to Production Systems

Generative models learn patterns in training data and use them to produce new outputs. For many large language models, the core training objective is next-token prediction: given a sequence, predict the next token, which may represent a word fragment, punctuation, or another encoded unit. Transformer attention allows a model to combine information from different positions in its context. During inference, the model generates tokens sequentially from a probability distribution. Instruction tuning and preference-based training can improve helpfulness and instruction following, but neither turns prediction into guaranteed factual reasoning.

Several controls affect application behavior. The context window limits how much input and generated output a model can accommodate, while output limits constrain response length. Lower temperature generally reduces sampling variability; it does not ensure correctness or universal repeatability. A model may still invent a policy because plausible language is not the same as verified evidence. For an internal support assistant, treat pretrained knowledge as a general language capability, not the authoritative source for company rules. Supply relevant evidence, require uncertainty when evidence is missing, and validate consequential outputs outside the model.

Begin with a task contract: accepted inputs, required outputs, authoritative data sources, permitted actions, and failure conditions. Our support assistant accepts an employee’s question and authenticated identity, retrieves policies that employee may access, and returns an answer with supporting references. It may also prepare a service request, but submission requires explicit confirmation. This separates an informational task from a state-changing operation. Start with a deterministic workflow when the steps are known; use an agent only when the system genuinely needs to choose among actions based on intermediate results.

Prompt engineering then expresses this contract clearly. Separate trusted instructions from retrieved documents and user input, label evidence, and specify an output schema. For example, require fields named answer, source_ids, and needs_clarification. Where supported, schema-constrained generation can improve structural reliability, but application code must still validate types and permitted values. Source identifiers must also correspond to documents actually retrieved. If required information is absent, return a clarification request rather than manufacturing details. Keep secrets out of prompts, and remember that textual separation alone cannot enforce access control or prevent prompt injection.

Retrieval-augmented generation, or RAG, supplies relevant external information at answer time rather than relying exclusively on model parameters. A typical ingestion pipeline extracts document text, preserves metadata, splits content into meaningful chunks, computes vector embeddings, and stores searchable representations. Embeddings encode semantic relationships as numeric vectors; similarity search finds nearby representations, not necessarily true or authoritative statements. Preserve document title, section, effective date, and access permissions with every chunk. Choose boundaries that keep rules and exceptions together, and test chunk sizes against real questions rather than assuming one universal setting.

At query time, enforce the employee’s permissions before unauthorized content can reach the model. Search can combine lexical matching, useful for exact identifiers, with vector search, useful for paraphrases; a reranker can then reorder candidates. Suppose the employee asks, “Can I expense a keyboard for my home office?” In this illustrative policy set, one retrieved passage allows peripherals up to $120, while another requires manager approval before purchase. A grounded answer should report both conditions and cite both passages. If only the spending limit is retrieved, the answer may sound correct while omitting a decisive restriction.

An AI agent architecture typically combines a model, explicit state, tools, and a control loop. The model proposes an action, the runtime validates and executes it, and the result becomes input for the next step. Tool calling is a structured request to application code—not direct execution by the model itself. Define narrow tools such as search_policy(query) and draft_request(item, estimated_cost, justification). Enforce argument schemas, authorization, and business rules in the runtime. Avoid broad tools that accept arbitrary shell commands or database queries when a purpose-built operation can satisfy the task.

Continuing the example, the assistant retrieves the keyboard policy, asks for the estimated cost, and drafts a request for $95. It presents the exact proposed action for confirmation before invoking a separate submission tool. Approval to submit the request is not the manager’s approval to purchase; those are different workflow states. Bind confirmation to the specific draft so changed arguments cannot reuse earlier approval. Use an idempotency key to prevent duplicate requests after retries. Cap tool calls, elapsed time, and spending, and define clear stopping conditions when evidence is insufficient, authorization fails, or a human decision is required.

LLM evaluation should test the system’s components as well as its final behavior. Build a versioned dataset of representative questions, expected evidence, acceptable answers, and prohibited actions. Include ambiguous questions, stale policies, conflicting sources, inaccessible documents, and requests outside the assistant’s scope. For retrieval, measure whether the necessary evidence appears among the returned candidates. For generation, check factual support, completeness, citation validity, and appropriate abstention. For agents, inspect whether the correct tools were chosen, arguments were valid, permissions were respected, and the intended workflow state was reached.

A worked test might define success for the keyboard question as mentioning both the $120 limit and prior manager approval, with citations that support each condition. An answer containing only the limit fails completeness even if every included statement is true. A request submitted without confirmation fails action safety even if its contents are accurate. Model-based judges can help scale assessment, but calibrate them against human-reviewed examples and do not treat their verdicts as ground truth. Re-run evaluations whenever prompts, models, retrieval settings, or tool definitions change; track latency and cost alongside quality.

Prompt injection occurs when untrusted content attempts to redirect the system—for example, a retrieved document telling the assistant to reveal employee records. Treat documents and tool responses as data, not authority. Defend with least-privilege credentials, access filtering, restricted network destinations, and human approval for consequential actions. Do not rely on a prompt that merely says “ignore malicious instructions.” Validate outputs before displaying them as executable content or passing them downstream. Logs and conversational memory also need privacy controls: redact sensitive fields, define retention periods, and separate tenants throughout storage and retrieval.

Reliability requires bounded retries, timeouts, fallback behavior, and observable execution traces. Record model and prompt versions, retrieved document identifiers, tool outcomes, token usage, and timing without unnecessarily storing private content. On AWS, Amazon Bedrock can provide access to foundation models and managed capabilities for retrieval and agent workflows, subject to model and regional availability. IAM permissions and application authorization still require deliberate design. Estimate request cost from model input and output usage, embedding work, retrieval infrastructure, and tool execution. Cache only where freshness and permissions permit; consider smaller models when evaluations show they satisfy the task.

A useful learning sequence follows increasing responsibility. First, build a structured-output assistant using a small, non-sensitive test corpus. Next, add retrieval and show exactly which passages support each answer. Then introduce a read-only tool, followed by a request-drafting tool and an approval-gated submission step. Finally, add adversarial tests, monitoring, and failure recovery. Keep a baseline at every stage so you can demonstrate whether each change improves outcomes. This progression teaches an important distinction: a successful demonstration proves possibility, while a repeatable evaluation and controlled deployment provide evidence of operational readiness.

Erudex’s Generative AI & AI Agents course supports this learning direction through tutor videos, hands-on labs, a management-console lab, quizzes at every lesson, and an Erudex certification. It also prepares learners for the AWS Certified AI Practitioner exam, AIF-C01, with the AWS certification awarded by AWS after its requirements are met. These credentials are separate: completing Erudex does not automatically confer AWS certification. AIF-C01 is foundational rather than proof of advanced production engineering. Pair certification preparation with implementation practice, documented evaluations, and clear explanations of architectural trade-offs to demonstrate deeper technical understanding.