MBA Training AI

Most professionals assume that longer, more detailed prompts produce better AI outputs. The real skill is something narrower: identifying which specific context actually changes the answer, and leaving everything else out.

Go deeper, free lessons

• The context window: the model's working memory
• Anatomy of a good prompt: role, context, task, constraints
• Common prompting mistakes and how to fix them
• Iteration and refinement: treating the model like a collaborator
• Working with long documents and big inputs

Full article and transcript: https://www.mba-training.com/blog/right-context-ai-prompting

MBA Training, mba-training.com

Most LLMs confidently answer questions using knowledge that stopped updating months or years ago. RAG fixes that, and this playbook shows you exactly how to build it without getting lost in the technical weeds.

Go deeper, free lessons

• Retrieval-augmented generation (RAG): giving models your data
• Choosing the right approach: prompt, RAG, fine-tune, or agent
• Evaluating outputs: how do you know it works?
• The AI tools landscape: APIs, no-code, and vector databases
• Hallucinations: why confident answers can be wrong

Full article and transcript: https://www.mba-training.com/blog/rag-explained-practical-playbook

MBA Training, mba-training.com

Klarna ran one of the most cited enterprise deployments of AI agents in financial services, and the results were genuinely mixed. Here is what actually happened, what the numbers mean, and what any organization should take from it before committing to agent-based automation.

Go deeper, free lessons

• Agents vs workflows vs automations: choosing the right level of autonomy
• Guardrails, permissions, and human-in-the-loop
• Evaluating and debugging agents: traces, evals, and failure modes
• Tools and function calling: giving your agent hands
• Cost, latency, and reliability: shipping agents to production

Full article and transcript: https://www.mba-training.com/blog/ai-agents-help-fail-klarna

MBA Training, mba-training.com

Coding assistants like GitHub Copilot, Cursor, and Claude have made it genuinely possible for non-engineers to build working software. But the dominant narrative around "vibe coding" is flattening a more complicated reality that professionals need to understand before betting on it.

Go deeper, free lessons

• ChatGPT for coding and canvas
• Claude code: an agentic coder in your terminal
• Codex: agentic coding in your environment
• Verifying outputs: trust but check
• Guardrails: permissions, review, and cost

Full article and transcript: https://www.mba-training.com/blog/vibe-coding-non-engineers-risk

MBA Training, mba-training.com

JPMorgan Chase deployed AI agents across legal review and trading operations, then discovered that automation without structured human checkpoints created compliance exposure it hadn't anticipated. The decisions they made to redesign those workflows offer a concrete template for any organization running agents at scale.

Go deeper, free lessons

• Guardrails, permissions, and human-in-the-loop
• Evaluating and debugging agents: traces, evals, and failure modes
• Agents vs workflows vs automations: choosing the right level of autonomy
• Agents and tool use: letting models take actions
• Hallucinations and verification in high-stakes work

Full article and transcript: https://www.mba-training.com/blog/human-oversight-ai-agent-workflows

MBA Training, mba-training.com

Sending a contract, a customer list, or internal financials into an AI tool feels like using a search engine. It is not, and the distinction carries real legal and competitive consequences.

Go deeper, free lessons

• Privacy and confidential data: what not to paste
• Governance and the EU AI Act: the topline
• Security, privacy, and data controls
• Tokens, training, and inference: what happens when you hit enter
• Workspace governance and data

Full article and transcript: https://www.mba-training.com/blog/business-data-privacy-ai-models

MBA Training, mba-training.com

Most professionals are still treating multimodal AI as a novelty rather than a daily workflow tool. This playbook shows you how to combine text, image, voice, and video capabilities into concrete business tasks, starting this week.

Go deeper, free lessons

• Images, voice, and vision
• Multimodality and long context: Gemini's superpowers
• Building your personal AI workflow
• Meetings, email, and admin: reclaiming hours each week
• Privacy and confidential data: what not to paste

Full article and transcript: https://www.mba-training.com/blog/multimodal-ai-business-tasks-playbook

MBA Training, mba-training.com

The challenge of getting an entire team to actually use a new technology is older than AI by several decades. The story of how organizations learned to do it well starts in a place almost no one remembers: a corporate fight over spreadsheets.

Go deeper, free lessons

• Building your personal AI workflow
• When AI helps and when it does not
• Sharing, governance, and custom gpts at work
• Ethics and responsible use at work
• Privacy and confidential data: what not to paste

Full article and transcript: https://www.mba-training.com/blog/ai-adoption-team-rollout-strategy

MBA Training, mba-training.com

Most AI assistants are islands. The Model Context Protocol is the specification that turns them into networked systems, and understanding how it works changes what you can realistically build or demand from AI in your organisation.

Go deeper, free lessons

• MCP explained: the USB-c for AI tools
• Connecting and using MCP servers
• Building your own MCP server
• Tools and function calling: giving your agent hands
• Connector safety, permissions, and governance

Full article and transcript: https://www.mba-training.com/blog/model-context-protocol-ai-tools

MBA Training, mba-training.com

Fine-tuning and retrieval-augmented generation solve different problems, and confusing the two leads to expensive mistakes. This article explains the mechanics of each approach and gives you a practical framework for choosing between them.

Go deeper, free lessons

• Choosing the right approach: prompt, RAG, fine-tune, or agent
• Retrieval-augmented generation (RAG): giving models your data
• Cost, latency, and model selection tradeoffs
• Evaluating outputs: how do you know it works?
• Scoping, data, and success criteria

Full article and transcript: https://www.mba-training.com/blog/fine-tuning-vs-rag-enterprise-ai

MBA Training, mba-training.com