Blog

PII masking helps hide personal data before sending a request to an LLM. We show where to place redaction, how to measure meaning loss, and how to safely return fields.

When to stop an AI agent: we look at risk signals in finance, healthcare, and law, and show where the agent should hand the task to a person.

Assistant personalization works better when you store only the signals that change the answer: role, language, request goal, and fresh context.

We’ll show how to break an LLM budget down by team, limits, and use cases so costs don’t grow after the pilot and the move to production.

Knowledge base search can be built with embeddings or a generative model. Here we cover indexing, reranking, and answers with citations.

What to store for prompt debugging: how to separate raw requests, masked copies, and metrics without exposing personal data.

Token usage forecasting helps you spot overspending early, set thresholds, catch model spikes, and avoid waiting for the invoice at month-end.

SLOs for LLM applications help connect latency, valid response share, and cost to business expectations, not to charts made for reporting.

Call summarization helps only when the call card shows the topic, outcome, risk, and next step without extra fields.

Source-based fact checking helps you build tests where the answer is compared against a document, table, or database. We’ll cover the test suite structure, common mistakes, and a checklist.

Evaluating models on your own data helps you choose the right LLM for product tasks: how to collect scenarios, gold answers, and metrics, and compare responses fairly.

Red teaming a corporate bot helps uncover data leaks, instruction bypasses, and toxic replies before release so you can fix them step by step.

Data deletion at a provider should not be checked by word of mouth. Before buying, ask for contract clauses, logs, cleanup timelines, and the audit process.

When fine-tuning pays off: we look at signs that a prompt has reached its limit, which tasks benefit most, how to estimate ROI, and common mistakes before launch.

Tool-output injection often hides in CRMs, emails, and HTML. Learn how to filter data, isolate tools, and add checks.

AI-powered review of credit and legal documents helps you spot risky clauses faster, but the final decision on the case still belongs to a specialist.

Different tokenizers across providers change price, limits, and the real context length. Let's look at where the calculations diverge and how to check them in advance.

LLM regressions are not always obvious right away. Here we break down daily runs, alerts, control cases, and the checks to do before users complain.

Switching between hosted and self-hosted models can reduce cost and latency if you separate use cases by time of day, data sensitivity, and load spikes.

We explain how to document patient consent for LLM use in a clinic: what to record before summarization, triage, and chart-based answers.

Error code normalization for LLM APIs helps reduce timeouts, limits, and bad requests into one dictionary for product, logs, and support.

We break down when a chain of models or one strong model gives the better result: comparing price, latency, quality, and the risk of unnecessary complexity.

We break down when two answers to one prompt help users choose tone, format, or action faster, and when that approach only creates confusion.

How AI agent state is stored affects pauses, approvals, and restarts. We look at when to choose Redis, a database, or an event log.

How to avoid overpaying for long context: we break down chat history trimming, compression, and dialog memory choices to preserve meaning and reduce tokens.

A sandbox for AI tools helps isolate writes in CRM, databases, and documents so the agent changes only the needed fields and does not get extra access.

A token spike after a release is easy to miss. Learn how to check prompt length, call frequency, retries, and strange post-release behavior before the bill arrives.

Learn what to log in an LLM app to debug failures, track incidents, and pass audits without storing prompts, PII, or extra data.

Query cache payback is easy to calculate with a simple formula. We show the repeat threshold for search, support, and email generation.

Let’s look at when a reranker pays off in search: how to measure recall gains, the impact on latency, request cost, and when the extra step is not worth it.

Show how AI content labeling works in the interface and where to place the label in an editor, CRM, and chat so it helps instead of getting in the way.

Model access policies help set rules by role, data, and environment so you can control costs and keep sensitive data from leaving your systems.

Deduplicating repeat requests helps remove double form submissions and duplicate chat messages, preserve UX, and avoid losing data during network and queue failures.

Customer complaint classification helps assign queues and SLAs faster when you combine simple rules, LLMs, confidence checks, and manual review.

Load testing an LLM service helps you find where queues grow, what breaks under peak load, and where the bottleneck sits in the API, network, and retries.

Feature flags for AI features let you enable new models by tenant without a global release: launch plan, checks, failures, and an example.

A prompt library helps a team keep working templates in one place: cards, tags, owners, examples, and an update routine.

Protect RAG from prompt injections: clean documents, limit tools, verify sources, and reduce the risk of false answers.

Product listing enrichment can be handled by a small local model when you need attributes, tags, and short descriptions without complex generation.

How to choose an internal LLM: compare open-weight models by size, languages, response format, and GPU needs on real-world tasks.

Choosing the right model type is easier when you run one domain dataset through summarization, extraction, classification, and chat, then compare the metrics.

Outgoing response moderation helps prevent risky text from slipping into chat, email, and CRM. We will look at where to place rules, filters, and a second model call.

Normalizing dates, currencies, and numbers helps bring LLM outputs into one format by removing inconsistency in dates, amounts, separators, and currency codes.

Session context and user profile should be stored separately so the assistant does not mix one-time details, preferences, history, and personal data.

Learn how to reduce dependence on a single vendor with an abstraction layer, compatibility tests, and step-by-step migration without a major refactor.

LLM context trimming helps keep a conversation within the token limit. We’ll cover context windows, priorities, conversation summaries, and quick checks.

Embedding dimensionality affects search, indexes, and storage schemas. We show where code breaks, where quality drops, and how to migrate safely.

A domain search glossary helps the system understand company terms, synonyms, and codes. Often it brings more accuracy than switching models.

Timeouts in an LLM chain affect the answer just as much as model choice. We’ll show how to split a shared SLA between the gateway, search, tools, and the model.

We show how to calculate the cost of LLMs in tenge for a year: tokens, exchange rates, traffic spikes, a test buffer, and a clear budget for the team.

Hedged requests to two models can remove rare slow responses, but sometimes they only double costs. Let’s break down thresholds, metrics, and mistakes.

Draft and action in the AI workflow help prevent a model from immediately changing a ticket status, limit, or record. Let’s break down the rule, steps, and checks.

External LLM provider outage: a step-by-step day-of guide for switching routing, adding limits, simplifying features, and coordinating teams.

Who should be allowed to change prompts in production? Let’s break down roles, review, change logs, and rollback so the team does not rely on private agreements.

Judge models for auto-evaluation help you check answers quickly, but not everywhere. Here is how to use a rubric, manual sampling, and signs of systematic errors.

A pre-release evaluation pipeline helps catch regressions before launch: how to build a golden set, choose metrics, and create a report people can read in 10 minutes.