Getting Started with LLM-Powered Workflows

Large language models have moved far beyond chatbot novelty. In 2026, they’re embedded in CI pipelines, documentation workflows, customer support systems, and code review processes. But getting started can feel overwhelming.

Why Workflows, Not Chat

The biggest misconception about LLMs is that they’re conversational tools. The real power emerges when you treat them as workflow components — discrete steps in a larger pipeline that accept structured input and produce structured output.

Consider a simple example: instead of asking an LLM to “review my code,” you build a pipeline that extracts the diff, formats it with context, sends it through a prompt template, and parses the structured response into actionable comments on your PR.

The Three Pillars

Every LLM workflow rests on three foundations:

1. Structured Prompting — Using templates, few-shot examples, and output schemas to get reliable, parseable responses
2. Context Management — Feeding the right information at the right time, whether through RAG, tool use, or carefully managed conversation windows
3. Output Validation — Never trusting raw LLM output; always validating, parsing, and handling failures gracefully

Your First Workflow

Here’s a minimal Elixir example using an LLM to auto-categorize incoming support tickets:

defmodule Support.Classifier do
  @prompt """
  Classify the following support ticket into exactly one category.
  Categories: billing, technical, account, feature_request, other

  Ticket: <%= ticket_text %>

  Respond with JSON: {"category": "...", "confidence": 0.0-1.0}
  """

  def classify(ticket_text) do
    prompt = EEx.eval_string(@prompt, ticket_text: ticket_text)

    {:ok, response} = LLM.complete(prompt, model: "claude-sonnet-4-6")

    response
    |> Jason.decode!()
    |> validate_category()
  end

  defp validate_category(%{"category" => cat, "confidence" => conf})
       when cat in ~w(billing technical account feature_request other)
       and conf >= 0.0 and conf <= 1.0 do
    {:ok, %{category: cat, confidence: conf}}
  end

  defp validate_category(_), do: {:error, :invalid_classification}
end

Notice the pattern: structured prompt → LLM call → parse → validate. This is the backbone of every reliable LLM workflow.

What’s Next

In the following posts, we’ll dive deep into each pillar — prompt engineering patterns, RAG architectures, agent frameworks, and production deployment strategies. The goal is always the same: making LLMs reliable, predictable, and genuinely useful in your day-to-day work.