← Retour aux guides

Production LLM Apps: 7 Myths That Cause Incidents

Myth 1: A Strong Model Eliminates the Need for Evaluation

Fact: Model quality is only one part of application quality. Production failures often come from ambiguous inputs, missing context, flawed retrieval, tool-call errors, and unsupported assumptions. Build a versioned evaluation set from real user tasks, including adversarial and edge cases. Score outputs against explicit criteria such as factual grounding, JSON validity, tool selection, refusal behavior, and task completion. Run the suite whenever you change prompts, models, retrieval settings, or tool schemas.

Use both offline and live evaluation. Offline tests catch regressions before release; sampled production traces reveal issues your curated set missed. Keep human review for high-impact workflows such as legal, financial, medical, or account-changing actions.

Myth 2: Prompt Engineering Is a One-Time Setup Task

Fact: Prompts are production configuration and should be treated like code. Store system prompts, few-shot examples, output schemas, and model parameters in version control. Record the prompt version with every request so an incident can be reproduced. Prefer narrow instructions, structured inputs, and explicit success criteria over long, vague prompts.

For machine-consumed responses, require a strict schema and validate it server-side. A model can still produce malformed or semantically invalid fields, so validate types, allowed values, lengths, and business rules before executing any action. When validation fails, retry with a focused repair instruction or route the request to a safe fallback path.

Myth 3: Tool-Using Agents Can Be Given Broad Permissions

Fact: An LLM should propose actions, not receive unrestricted authority. Give each tool the smallest possible permission set, enforce authorization outside the model, and require confirmation for irreversible operations. Never let model-generated text become shell commands, database queries, URLs, or API parameters without parsing and validation.

Defend against prompt injection by separating untrusted content from instructions. Mark retrieved documents, web pages, emails, and user uploads as data. Do not allow those sources to override system policy or tool permissions. Use allowlists for tools and destinations, cap tool-call loops, and log every attempted action with its approval result.

Myth 4: Retrieval-Augmented Generation Automatically Makes Answers Accurate

Fact: Retrieval improves grounding only when the right material is retrieved and used correctly. Chunk documents by logical sections, preserve source metadata, and test chunk size and overlap against real questions. Retrieve a small, relevant set, then instruct the model to answer only from that context and acknowledge when evidence is missing.

Measure retrieval separately from generation. Track whether the expected source appeared in the top results, whether citations support the final claim, and whether stale content was used. Re-index when source documents change, apply document-level access controls before retrieval, and avoid placing sensitive content in prompts unless the user is authorized to see it.

Myth 5: One Provider or Model Is Enough for Every Request

Fact: Production systems need intentional routing. Use lower-cost, faster models for classification, extraction, summarization, and simple support tasks. Reserve stronger models for complex reasoning, difficult coding, or high-value decisions. Set budgets for tokens, latency, retries, and tool calls per request.

A relay can make multi-model routing easier without forcing SDK rewrites. 59API provides pay-as-you-go access to official-quality Claude models, including Opus, Sonnet, Haiku, and Fable, plus GPT models through https://api.59api.com. Its compatibility with OpenAI SDKs, Claude Code, and Codex helps teams test model tiers while keeping integration work low. Its low pricing and referral rebate are useful when experimentation and evaluation generate substantial token volume.

Myth 6: Logging Prompts and Responses Is Sufficient Observability

Fact: Raw logs are difficult to use during an incident and can create privacy risk. Emit structured traces with request IDs, model and version, prompt version, token counts, latency, retry count, retrieval sources, tool calls, validation results, and final outcome. Redact secrets and personally identifiable information before storage, and define retention limits.

Create alerts for rising error rates, schema-validation failures, timeout spikes, cost anomalies, and unexpected tool activity. Monitor quality signals too: abandonment, user corrections, escalation rate, and sampled evaluator scores. A fast response that solves nothing is not a healthy system.

Myth 7: Reliability Means Retrying Every Failure

Fact: Retries require boundaries. Use exponential backoff with jitter for transient provider errors, but cap attempts and enforce a total request deadline. Make tool executions idempotent with request keys so a retry cannot duplicate a charge, email, or record update. Define graceful fallbacks: return a partial answer, queue asynchronous work, switch to a suitable model, or clearly tell the user the task cannot be completed now.

Start with a small production surface, instrument it thoroughly, and expand after real measurements validate quality, safety, latency, and cost. For teams seeking affordable model access during that iteration cycle, signing up for 59API is a practical way to connect compatible applications to Claude and GPT models without committing to a large upfront spend.

Tarifs · Docs