Mastering Prompt Engineering for Better AI Results
Prompt engineering is a crucial step in achieving accurate AI results. It involves designing and optimizing prompts to elicit specific responses from AI models.
AIS Tech Blog
Practical notes on web systems, automation, AI implementation, and digital delivery.
Mobile App Development: iOS, Android and Cross-Platform Solutions Compared
Mobile app development involves choosing between iOS, Android, and cross-platform solutions. Each option has its own set of advantages and disadvantages. By und
Customer service chatbots: safe tools and human handoff
Scope customer service chatbots around maintained answers, server-side permissions and human handoff. Check licensing limits and pilot failure cases before roll
AI workflow orchestration: control retries and approvals
Design AI workflows with durable state, limited tools and explicit approvals. Handle retries, partial failures and recovery before automating business actions.
Database design for growth: integrity before partitioning
Design a business database around stable identifiers, constraints and measured queries. A runnable SQLite example shows a relationship and a useful lookup index
MCP integration: tools, context and permission boundaries
Understand what Model Context Protocol standardizes and what it leaves to your application. Plan tools, resources, authorization and tests without invented SDK
POS and webshop inventory sync: ownership and reconciliation
Connect retail POS and e-commerce inventory with stable product IDs, reservation rules and recovery. Test duplicate events, returns and simultaneous purchases.
AI document search: retrieve the query and respect revisions
Build construction document search around query matching, approved revisions and access. A runnable lexical baseline demonstrates query ranking without claiming
Multi-language websites: URLs, formats and editorial control
Plan multilingual content with stable language URLs, correct locale formatting and review. A complete JavaScript example formats a price without changing its cu
API integration risks: timeouts, duplicates and permissions
Reduce API integration risk through contracts, operation IDs, permission checks and reconciliation. Distinguish rejected requests from unknown outcomes before r
AI automation ROI: a five-step decision with real costs
Evaluate AI automation through a measured baseline, limited pilot and complete operating costs. A hypothetical calculation separates saved time from cash saving
AI scaling costs: budget the completed workflow
Budget AI deployment by completed tasks, context size, retries and human review. Build normal and peak scenarios with spending controls and current provider ter
Evaluate coding models with repeatable repository tasks
Compare coding models using representative tasks, hidden tests, review effort and regression risk. Avoid toy examples that do not reflect your repository.
We Built an AI Blog That's Being Read by More AIs Than Humans (And Why That's Fine)
Most web content is now consumed by AI systems before humans ever see it. Here's what that means for your content strategy — and why optimizing for AI readers m
Web application security: test ownership and permission
Protect business web applications with server-side authorization, validated inputs and secure operations. Test cross-user access, exports and sensitive workflow
E-commerce traffic spikes: protect checkout, not just pages
Prepare e-commerce for peak traffic with cache boundaries, inventory consistency and payment recovery. Test the complete order flow under representative load.
Internal web applications: scope approvals and exceptions
Build an internal application around one complete workflow, explicit roles and recovery. Define approval conditions and data ownership before designing screens.
Booking systems: coordinate slots, payment and cancellation
Design booking systems with explicit reservation state, payment confirmation and calendar ownership. Handle simultaneous requests and cancellation before launch
AI reasoning modes: match the task and output budget
Choose reasoning settings by task, latency and cost. Verify model-specific output limits, test incomplete answers and keep internal reasoning out of public cont
API integration planning: define the business contract first
Plan an API integration through record ownership, mapping, credentials and acceptance. Define one complete business operation before selecting a connector or pl
Introduce AI coding assistants with review and team controls
Roll out a coding assistant through approved context, small diffs and meaningful tests. Measure reviewed delivery and rework while protecting repository and cus
Choose a coding model by context, deployment and cost
Select a coding model using repository context, supported tools, privacy requirements and completed-task cost. Separate model choice from workflow evaluation.
Reliable AI agents: evidence, tools and controlled failure
Reduce unsupported AI answers with traceable retrieval, limited tools and refusal. Test permission boundaries, hostile documents and recoverable action failures
Gemini 2.0 Flash retirement: migrate the business workflow
Gemini 2.0 Flash is retired according to Google's model lifecycle page. Plan replacement by testing output, tools, latency and cost on your existing workflow.