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Building with AI

8 quick wins, collected in one place. Each one is short on purpose: a specific job, what to hand the AI, and where a human still has to stand.

Prompt Engineering: The Practical Guide for 2026

How to write effective AI prompts in 2026. Techniques, patterns, common mistakes.

Prompt engineering is the practice of writing instructions for LLMs to get desired outputs. Critical skill for AI users in 2026.

Effective prompt patterns

Specific request: "Write a professional email" → "Write a 100-word email to my landlord requesting a meeting about lease renewal, formal tone."

Role + context: "You are an experienced [role]. Given [context], do [task]."

Examples: Provide 2-3 examples of desired output format.

Constraints: Specify length, format, tone, what to avoid.

Step-by-step: Break complex tasks into steps.

Common mistakes

  • Vague requests
  • Missing context
  • No examples
  • Asking too much in one prompt
  • Trusting first output

Advanced techniques

  • Chain-of-thought: Ask AI to show reasoning
  • Few-shot: Provide examples in prompt
  • System prompts: Set persistent context
  • Iterative refinement: Edit and re-prompt

Bottom line

Prompt engineering is a practitioner skill. Better prompts produce better outputs. Worth the effort.

AI Build vs Buy Decision Framework

When to build custom AI vs buy off the shelf. Decision framework.

Build vs buy is fundamental AI strategy decision.

Buy when

  • Standard capability widely available
  • Vendor will out-innovate internal build
  • Speed to value matters
  • Limited engineering resources
  • Compliance requirements complex

Build when

  • Workflow specific to firm
  • Differentiation strategic priority
  • Integration depth required
  • Long-term economics favor build
  • Available engineering capability

Hybrid (most common)

Buy foundation tools, build differentiation on top. Most enterprises hybrid.

Bottom line

Match approach to strategy. Pure buy or build rarely optimal.

AI Infrastructure Architecture for Business

How to architect AI infrastructure. Cloud, hybrid, on-premise considerations.

AI infrastructure architecture affects performance, cost, compliance.

Cloud-native

AWS, Azure, GCP with AI services. Fastest deployment. Scales easily. Most enterprises start here.

Hybrid

Some on-premise, some cloud. Sensitive data on-premise; compute in cloud. Common in regulated industries.

On-premise

Full control. Higher cost. Required for some compliance situations.

Edge

AI close to data source. Real-time inference. Bandwidth optimization.

Bottom line

Most enterprises cloud-first with hybrid for specific needs. Match architecture to requirements.

AI Deployment Patterns for Business

Common patterns for deploying AI in business. From pilot to production.

AI deployment patterns guide successful production deployments.

Common patterns

Wrapper: Wrap existing process with AI layer. Lowest risk, lowest reward.

Embed: AI inside existing applications. Moderate risk and reward.

Replace: AI replaces existing process. Higher risk, higher reward.

New capability: AI enables previously impossible capability. Highest risk and reward.

Production considerations

Latency, accuracy, cost, monitoring, fallback. Each requires specific architecture decisions.

Bottom line

Match deployment pattern to risk tolerance and value potential.

AI Evaluation and Testing for Business

How businesses evaluate AI quality. Benchmarks, A/B testing, ongoing monitoring.

AI evaluation determines what works. Without evaluation, AI deployment is hope.

Evaluation approaches

Offline benchmarks (specific test sets), A/B testing in production, user feedback, business metrics.

Common pitfalls

Generic benchmarks don't translate to specific use cases. Cherry-picked examples mislead. Single metric oversimplifies.

Continuous monitoring

Quality drift over time as data and context change. Need ongoing evaluation. Production monitoring essential.

Bottom line

AI evaluation is operational discipline. Skip at cost.

AI Monitoring in Production

How to monitor AI systems in production. Quality, performance, drift.

Production AI requires monitoring. Quality drift, performance issues, cost surprises all happen.

What to monitor

Quality (output correctness), performance (latency, throughput), cost (per query, per user), errors, user satisfaction.

Quality drift

Models perform differently as data and context change. Without monitoring, quality degrades unnoticed.

Tools

Datadog, Splunk with AI features, specialized AI monitoring (Arize, Fiddler, WhyLabs).

Bottom line

AI monitoring is operational discipline. Skip at cost.

AI Evaluation Platforms Compared

Comparing platforms for evaluating AI models in production. Use cases, strengths.

AI evaluation and monitoring platforms compared.

Arize AI

Strong production monitoring. Drift detection. Embedding analysis.

Fiddler

Model performance and bias monitoring. Enterprise focus.

WhyLabs

Open source plus managed. Data quality plus model monitoring.

LangSmith

LangChain-specific. Strong for LLM applications. Tracing focused.

Bottom line

Choose based on stack. LangSmith for LangChain. Others for broader production needs.

LangChain vs LlamaIndex: AI Framework Comparison

Comparing two major AI application frameworks. Use cases, strengths, choosing.

Two major AI application frameworks. Different strengths.

LangChain

Broader scope. Chains, agents, memory, tools. Larger community. Some criticism for complexity.

LlamaIndex

Specialized in RAG and retrieval. Strong document handling. Often combined with LangChain.

Combination

Many use both. LangChain for orchestration, LlamaIndex for retrieval.

Bottom line

Not exclusive. Match to use case. Combine where appropriate.