ML Case-study Interview Question: Advanced LLM Prompting Techniques for Accurate and Scalable E-commerce Productivity Tools
Browse all the ML Case-Studies here.
Case-Study question
You are tasked with improving a large e-commerce platform’s internal productivity workflows using Large Language Models. The firm uses a variety of LLM-based solutions: generating concise pull request titles and descriptions, brainstorming feature ideas, classifying short text snippets, and automating responses to questions from internal teams. LLM inaccuracies (e.g., hallucinations) still occur, and you must propose a solution strategy. How would you integrate advanced prompt techniques to ensure robust, scalable, and cost-effective outcomes? Outline your design for the entire workflow, including system architecture, prompt methodology, and strategies for quality control.
Proposed Solution
Overview
Use an LLM-based system enhanced with carefully crafted prompts, external data lookups, and iterative validations. Employ a conversation-driven approach to reduce hallucinations, maintain consistency, and produce outputs in flexible formats that you can process programmatically.
System Architecture
Feed user requests or code changes into a pipeline that:
Preprocesses and categorizes the request.
Selects the relevant prompting technique (e.g., brainstorming or classification).
Optionally consults external tools (search, calculations).
Produces a structured response for downstream tasks.
Wrap the LLM inside an API layer that handles multiple turns:
Round 1: Generating outlines or multiple drafts.
Round 2: Selecting the best answer or refining.
Round 3: Applying forced constraints or classification tokens.
Prompt Methodology
Room for Thought Ask the model to produce an outline or plan before generating a final answer. Example: prompt it to list categories or bullet points first, then request the actual result. This clarity increases focus and prevents rambling.
Monte Carlo Request multiple potential answers or options. Combine the best parts into a single result. This fosters creativity and helps overcome the model’s tendency to fixate on a single suboptimal solution.
Self Correction Prompt the model to generate answers, critique them, then produce a final refined answer. Include reminders about desired standards or guidelines.
Classifying Constrain responses to a small set of permitted tokens. Provide them as ‘000’, ‘001’, etc. For multi-turn workflows, let the model do the reasoning first, then force it to pick one token. Set temperature to zero and use logit_bias to nudge it toward a specific set of responses.
Puppetry Prepend partial statements to the model’s context, making it believe it started replying in a particular format (for example, JSON). This coerces the model to continue in the same style without producing extraneous text.
Quality Control
Test each prompt design offline with multiple examples. Evaluate correctness, cost, and latency. Monitor for hallucinations using double-check patterns:
For creative tasks (e.g., brainstorming): Follow up with internal inspection or a second LLM pass.
For classification tasks: Force multiple-turn logic plus numeric-coded answers.
Use a combination of environment variables (e.g., top_p, temperature) to fine-tune the model’s diversity of outputs.
Example Code Snippet
Below is a short Python example showing how to apply the “two-turn” approach for classification:
import openai
def classify_text(text, deep_thoughts=True):
# Round 1: Ask for reasoning
if deep_thoughts:
round1_prompt = f"""
You will analyze the statement and reason about it step by step:
"{text}"
Write down your reasoning now, but do not provide a final answer.
"""
response_round1 = openai.ChatCompletion.create(
model="gpt-4",
messages=[{"role": "user", "content": round1_prompt}],
temperature=0.7
)
# We don't use the direct output, just store it or log it
# Round 2: Force the final numeric-coded answer
round2_prompt = f"""
Consider the statement: "{text}"
Possible Answers:
000 True
001 False
002 Uncertain
Just pick the best match by writing one numeric code.
"""
# logit_bias to favor only tokens "000", "001", "002"
# (token IDs vary for each model; you must retrieve actual IDs for them)
# Example of partial usage:
response_round2 = openai.ChatCompletion.create(
model="gpt-4",
messages=[{"role": "user", "content": round2_prompt}],
temperature=0.0,
max_tokens=1,
logit_bias={"token_id_of_000": 100, "token_id_of_001": 100, "token_id_of_002": 100}
)
return response_round2.choices[0].message["content"]
This ensures a single numeric answer, while still giving the model a chance to reflect.
How would you address these follow-up questions?
1) How do you handle hallucinations in generation tasks that require factual content?
Include external data lookups or references. Let the LLM attempt to answer but give it an option to call a search or a function if the query touches external facts. Then re-inject the search results into the model context for final output. For example, instruct the model to respond with a special tag (e.g., GOOGLE_SEARCH: ) when it recognizes a need to check facts. On the second pass, supply the result and ask it to finalize.
Use cross-checks. For instance, after the LLM generates an answer, prompt it again for contradictions, then weigh the responses. On important tasks, use zero temperature to reduce randomness.
2) Why might using a large temperature be risky in certain pipelines?
High temperature increases sampling diversity, which can be valuable for brainstorming. But classification or regulated tasks need consistency. If the temperature is large, the system might pick improbable tokens and produce inconsistent or invalid outputs.
3) How do you ensure the LLM’s reasoning remains hidden when needed?
Keep the “thinking” or “planning” steps in separate prompt rounds. For final user-facing output, prompt the LLM to provide only the final concise answer. If the LLM tries to leak internal reasoning, remind it to revise or mask that section.
4) How do you scale these methods to handle thousands of requests?
Use a multi-tier strategy. Cache frequent or repeated prompts, especially for classification. Batch small tasks if they share context. Where possible, pre-generate certain partial prompts or standard instructions. Monitor performance to decide when GPT-3.5 might suffice instead of GPT-4, balancing cost and accuracy.
5) How do you verify correctness of creative outputs like generated titles?
Have the model produce multiple candidate results. Either prompt it for self-correction or do a separate pass with a narrower classification prompt that picks the best among the candidates. Optionally include a human-in-the-loop stage for final checks when brand or legal constraints matter.
6) Why would you store your model interactions in a system-level log?
To audit and revisit prompts that cause errors. This log helps identify misconfigurations in temperature or classification bias, enabling better iteration of your prompt designs. It also helps train smaller specialized models on your domain data. Keep security in mind if storing user queries or sensitive content.
7) Could you elaborate on optimizing cost?
Use smaller models for simpler tasks (e.g., short classification). Employ GPT-4 only for tasks requiring deep reasoning or high accuracy. Add fallback logic if an answer passes a certain confidence threshold. Minimize tokens by trimming your prompt context, removing repetition, and limiting extraneous text.