Prompt Engineering Questions

Zero-shot prompting means asking the model to do a task with no examples, just instructions. Few-shot prompting adds a handful of examples so the model can infer the pattern.

In a data science workflow, I rely on few-shot examples when the output must match a very specific format, such as classifying customer feedback into internal taxonomy labels or generating SQL in a house style. Examples reduce ambiguity and improve consistency.

The trade-offs are:

• Better accuracy and format adherence
• Higher context usage, which raises cost and can crowd out important input
• Risk of biasing the model if the examples are narrow or unrepresentative

For broad, simple tasks I start zero-shot. For high-stakes or structured outputs, I add a small, carefully chosen example set.

I’d treat this as a constrained text-to-SQL problem, not a free-form chatbot.

Prompt design

• Provide schema, table relationships, metric definitions, and allowed business terms.
• Instruct the model to only use approved tables/columns and to ask a clarifying question when a metric is ambiguous.
• Require it to return SQL plus a brief assumption log.

Guardrails and validation

• Parse SQL and block DDL/DML, cross joins without keys, and queries that bypass row-level security.
• Validate column references against the catalog and enforce semantic checks for metrics like “revenue” or “active user.”
• Add query cost limits, row limits, and a review step for high-risk queries.
• Run the generated SQL against a sandbox first, then compare output to expected grain and filters.

Example
If a user asks for “monthly retention,” I’d force the model to specify the cohort definition before execution. That prevents misleading outputs and makes the result auditable.

Prompt engineering is the practice of designing instructions so an LLM produces more reliable, useful outputs. For a data scientist, that means being explicit about task, context, constraints, and output format.

A strong prompt usually includes:

• Role and objective: What task should the model perform?
• Relevant context: Definitions, schema, business rules, or examples
• Constraints: Time range, allowed columns, tone, or assumptions
• Output structure: JSON, bullets, table, or SQL only

For SQL generation, I would specify table names, key relationships, and edge cases like null handling. For summarization, I would ask for grounded claims and a fixed length. For classification, I would provide label definitions and a few examples.

The goal is to reduce ambiguity and make outputs easier to validate and automate.

These parameters control how the model samples the next token.

Temperature lowers or raises randomness.

lower temperature -> more deterministic outputs
higher temperature -> more varied outputs

Intuition: For factual tasks, I’d keep it low so the model stays consistent.

Top-p samples from the smallest set of tokens whose cumulative probability reaches p.

top-p = 0.9

Intuition: It trims the long tail of unlikely words while still allowing some diversity.

Max tokens caps response length.

max_tokens = 200

Intuition: It prevents overly long answers and controls cost.

Stop sequences tell generation when to end.

stop = ["\n\nUser:", "###"]

Intuition: They keep the model from drifting into the next turn or format.

For a deterministic analytics assistant, I’d use low temperature, tight top-p, short max tokens, and explicit stop sequences. For brainstorming, I’d raise temperature and top-p to encourage more diverse ideas.