Foundational Techniques, Parameter Optimization, and Mitigating Hallucinations
Introduction
Building on our previous exploration of the psychology behind Large Language Models (LLMs) , this blog dives into actionable strategies to tackle hallucinations, inconsistencies, and inefficiencies in LLM outputs. Part 1 breaks down six core prompt engineering techniques, complete with implementation guidelines, parameter tuning tips, and a clear look at their trade-offs. Part 2 will venture into advanced frameworks like ReAct and PAL – stay tuned!
Key Terminology
- Prompt: Think of it as giving crystal-clear instructions to your model, guiding it on how and what to perform with precision.
- Hallucination: When an LLM confidently states something factually incorrect, like inventing non-existent regulations. We want to minimize these!
- Temperature: This dial controls randomness. 0.0 = laser-focused & predictable, 1.0 = wildly creative & exploratory.
- Top_p: A filter for token probabilities. 0.9 means considering only the top 90% most likely tokens.
- Token: The building blocks of text. Roughly, 1 word ≈ 1.5 tokens.
1. Chain of Thought (CoT): Structured Reasoning
What It Solves: Logical errors and inaccuracies in complex, multi-step tasks requiring reasoning.
Mechanics: Chain of Thought prompting mimics human step-by-step problem-solving. By explicitly prompting the LLM to articulate its reasoning process before giving the final answer, we guide it to break down complex problems. This is crucial because LLMs, while powerful, can stumble on direct multi-step inference, leading to logical leaps or errors.
Problem: “A person is 170 cm tall and weighs 85 kg. Calculate their BMI and categorize them as underweight, normal, overweight, or obese.”
Prompt:
Step 1: Calculate BMI → BMI = weight (kg) / [height (m)]^2. Height in meters = 170 cm / 100 = 1.7m. BMI = 85 kg / (1.7m)^2 = 29.4
Step 2: BMI Categories:
– Underweight: < 18.5
– Normal weight: 18.5 – 24.9
– Overweight: 25 – 29.9
– Obese: ≥ 30
Step 3: Category → Based on BMI 29.4, the person is **Overweight**.
Parameters: temperature=0.1 (strict logic), max_tokens=500 (allow step-by-step).
Trade-offs:
✅ Enhanced Accuracy: Significantly reduces logical errors in complex tasks. Studies, like Wei et al. (2022), show error reductions up to 60% by forcing explicit reasoning steps, especially in arithmetic and common-sense reasoning.
❌ Increased Latency & Token Usage: Step-by-step reasoning naturally increases response time. The LLM generates more tokens to explain its thought process, leading to ~30% slower responses and higher token consumption. Consider this trade-off for time-sensitive apps or large-scale prompt processing.
Best Practice: Use clear delimiters (e.g., “Step 1:”, “Reasoning:”, “Conclusion:”) to structure reasoning steps and guide the LLM. Try “Let’s think step by step:” at the prompt’s start for even more detailed reasoning!
2. Few-Shot Learning: Pattern Replication
What It Solves: Inconsistent output formats, especially for structured data like JSON or XML, where schema adherence is key.
Mechanics: Few-shot learning uses the LLM’s ability to recognize and copy patterns from just a few examples in the prompt. Instead of massive fine-tuning, we show the desired input-output format with examples. The LLM learns “in-context” what output is expected. Perfect for enforcing consistent formats without heavy retraining.
Example 1:
Input: “Snow tires, 225/50 R17, grip in ice” → Output: {“tire_type”: “snow”, “size”: “225/50 R17”, “feature”: “ice grip”}
Example 2:
Input: “All-season tires, 235/45 ZR18, comfortable ride in summer” → Output: {“tire_type”: “all-season”, “size”: “235/45 ZR18”, “feature”: “summer comfort”}
Task: “Recommend tire for ‘Audi A4, winter driving, prioritize safety'”
Parameters: temperature=0 (strict replication 🪞), top_p=0.95.
Trade-offs:
✅ Format Consistency & Precision: Ensures highly consistent output formats, crucial for API interactions, data extraction, and config file generation.
❌ Token Overhead & Potential Overfitting: Each example uses tokens, increasing cost & latency (200-500 tokens/example is significant!). Outputs can be wrong if input deviates semantically from examples.
Failure Mode: Overfitting to syntax, not semantics. May not be able to cover complex examples & scenarios.
3. In-Context Learning: Domain Expertise
What It Solves: Generic or superficial responses in specialized domains where nuanced understanding and domain-specific knowledge are essential.
Mechanics: In-context learning injects relevant domain-specific info directly into the prompt, giving the LLM a temporary “knowledge boost.” By including context like document excerpts, knowledge base articles, or guidelines, we bias the LLM’s response towards that domain.
Example:
Context: “Building codes in California require seismic retrofitting for houses built before 1980. Foundation bolting and bracing are key techniques.”
Query: “I have a house built in 1975 in San Francisco. What are my options to make it earthquake-resistant?”
Parameters: temperature=0.3 (balance accuracy/creativity).
Trade-offs:
✅ Domain-Relevant & Accurate Answers: potential to significantly reduces generic responses and boosts accuracy & relevance in specialized fields.
❌ Context Relevance & Noise Sensitivity: Effectiveness depends on context relevance & quality. Irrelevant or “noisy” context (too much extra info, poor summaries) can confuse the LLM, resulting in poor outcome
4. Role-Based Prompting: Add Expertise
What It Solves: When a specific tone, perspective, or expertise level is needed.
Mechanics: Role-based prompting uses the LLM’s ability to link language styles, vocab, and perspectives to roles or personas learned from training data. By telling the LLM to “act as” a professional (e.g., “Senior Lab Technician,” “Marketing Guru,” “Legal Expert”), we activate these learned stylistic patterns.
Example:
“Act as a Senior Lab Technician specializing in hematological disorders. Explain the complete blood count (CBC) test to a patient in simple terms, emphasizing the importance of each component like WC, RBC & platelets.”
Parameters: temperature=0.2 (professional tone), max_tokens=1000.
Use Case: Technical docs, compliance reports, professional comms drafts.
Trade-offs:
✅ Enhanced Tone & Style: Greatly improves output tone, style, and vocab, making it more professional, authoritative, or persona-aligned.
❌ Superficial Expertise & Potential Stereotyping: Role-based prompting improves style but doesn’t give real domain expertise. ” Over-relying on role-based prompting without factual grounding can mislead
5. Tree of Thought (ToT): Parallel Analysis
What It Solves: Encouraging exploration of multiple perspectives & reasoning paths, especially for complex problem-solving or strategic planning.
Mechanics: Tree of Thought (ToT) prompting goes beyond linear generation by prompting as in case of COT above the LLM to explore parallel reasoning paths or “branches” for complex questions. Instead of the first plausible answer, ToT encourages wider solution space exploration. It’s like LLM with multi-faceted decision-making.
Example:
Problem: “Develop a strategy to mitigate supply chain disruptions for a global electronics manufacturer.”
Branch 1 (Resilience): “Diversify suppliers across multiple geographic regions. Assess risks in each region.”
Branch 2 (Cost): “Analyze inventory holding costs vs. potential disruption costs. Explore near-shoring vs. off-shoring”
Branch 3 (Agility): “Implement real-time supply chain monitoring and predictive analytics. Develop flexible manufacturing capacity.”
Final Strategy: Prioritize supplier diversification and real-time monitoring while carefully evaluating near-shoring for critical components.
Parameters: temperature=0.4 (explore options), top_k=50.
Trade-offs:
✅ Reduced Blind Spots & Improved Decision Quality: Significantly reduces overlooking critical factors & biases in linear reasoning. Leads to more robust, informed decisions, especially for strategic planning, risk assessment, and complex problem-solving.
❌ Increased Computational Cost & Latency: ToT increases cost & latency as the LLM generates & processes multiple reasoning branches. Expect ~2x+ token cost & response time vs. single-path prompting.
6. Task-Based Prompting: Workflow Structuring
What It Solves: Providing a clear workflow and breaking down the task into manageable steps. Turn chaos into clear, actionable plans! ️
Mechanics: Task-based prompting is about giving the LLM a structured workflow or step sequence for a complex task. Instead of asking for one big output, we break down the task into smaller, defined sub-tasks. This gives the LLM a clear roadmap. We reduce ambiguity & help the LLM focus on specific deliverables for each workflow stage.
“Generate a 30-day content creation plan for a tech startup blog:
- Week 1: Keyword research & topic ideation (focus on cloud computing, AI, cybersecurity).
- Week 2: Draft 4 blog posts (2 thought leadership, 2 tutorials).
- Week 3: Review & edit posts, schedule social media promotion.
- Week 4: Publish posts, analyze website traffic & engagement.”
Best Practice: Use numbered lists, bullet points, or headings to delineate workflow steps. Give actionable, specific instructions for each step for clarity & effective LLM guidance.
Trade-offs:
✅ Structured, Comprehensive & Actionable Outputs: Ensures well-structured, comprehensive, and actionable outputs, especially for complex multi-stage projects. Ideal for project plans, reports, procedural docs, and any task needing a clear, step-by-step approach.
❌ Potential Rigidity & Reduced Flexibility: Overly rigid or detailed workflows can limit LLM creativity & flexibility. Prescriptive prompts might prevent exploring better solutions outside the defined workflow.
Part 2 Preview: Advanced Techniques
In Part 2, we’ll take these foundational techniques to the next level, exploring enterprise-grade pipelines with:
- ReAct: Combine reasoning + API calls (e.g., “Fetch stock prices → analyze trends”).
- PAL: Solve math problems via embedded Python code.
- Reflexion: Self-correction loops (“Verify Step 2 for errors”).
- ART: 🪄 Automatic tool-use (call APIs/databases mid-task).
- Prompt Chaining: How can we chain multiple prompts together to make robust prompt
Get ready to supercharge your prompt engineering skills!
“Which prompt engineering challenge keeps you up at night? 🌃”
- Hallucinations 🤥
- Slow response times 🐌
- Inconsistent formatting 😵
- Other (comment below!) 👇
Happy Learning & Let us know your thoughts or any other techniques that you have tried and worked wonders!!!!!
