IVQA 901-950

901. Measuring Accuracy of Multi-step Reasoning

• Use task-specific ground truths or intermediate checkpoints. Compare step-wise answers using logical correctness, not just final result.

902. Benchmarks for Chain-of-Thought (CoT) Quality

• Examples:

  • GSM8K (math)

  • StrategyQA (reasoning)

  • HotpotQA (multi-hop) Metrics: step accuracy, logical coherence, answer traceability.

903. Testing Consistency Across Tasks

• Repeat same inputs over time or under varied load. Use deterministic decoding or track divergence via embedding distance and token-level diffing.

904.Metrics for Helpfulness Beyond Task Completion

• Engagement length, user rating, guidance quality, clarification offered, and number of helpful reformulations or tool suggestions.

905. Identifying Tool/Step Hallucinations

• Cross-reference tool calls with allowed list. Log invocation traces. Use rule-based filters or semantic validation of step descriptions.

906. Rationality Scaffolding

• Providing structured steps like “plan → reason → act”. It improves coherence, reduces hallucination, and aligns with cognitive tracing.

907. Evaluating Task Decomposition

• Check if subtasks are logically sequenced and scoped. Validate decomposition against expert-curated task graphs.

908. Logging Structure for Replay

• Use JSON logs with:

  • step_id, input, output, tool_used, memory_context, timestamp. Enables replay and debugging.

909. A/B Testing Agent Policies

• Split traffic or use simulated task sets. Compare completion rate, user ratings, quality scores, and retry count.

910. Evaluating Emergent Behavior

• Look for patterns not seen in base training, e.g., agent collaboration, novel task breakdowns. Use clustering and behavior audits.

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911. Preserving State in Long Conversations

• Store evolving memory: user profile, session context, goals. Use memory compression or summarization for long histories.

912. Detecting Topic Shifts

• Use intent classifiers or embedding-based clustering. Topic transition thresholding using cosine similarity on recent messages.

913. Guiding Users with Unclear Intent

• Tactics:

  • Clarifying questions

  • Offer multiple interpretations

  • Confirm assumptions: “Did you mean X or Y?”

914. Handling Ambiguous Follow-ups

• Techniques:

  • Slot filling

  • Context window re-evaluation

  • Prompt agents to explicitly confirm ambiguity

915. Reset/Pause/Bookmark in UX

• Add UI actions for state snapshot. Persist memory with labels (e.g., “bookmark: tax conversation”). Support rollback or branch.

916. Controlling Verbosity Across Turns

• Use user preference (concise/detailed), or token budget tuning. Set verbosity flags in memory or model instructions.

917. Dialogue Guards Against Prompt Hijacking

• Input sanitization, boundary tokens, prefix constraints. Use LLM classifiers to filter adversarial prompts.

918. Response Chaining for Coherence

• Use previous response + summary as part of next prompt input. Ensures continuity and grounding in past context.

919. Testing for Regression Errors

• Snapshot prior conversations and rerun across model upgrades. Diff responses and score divergence.

920. Dynamic Temperature/Top-p Control

• Use conversation phase (intro = high creativity, summary = low). Adjust via feedback loops (e.g., if user is confused, reduce entropy).

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921. Combining Dense and Sparse Retrieval

• Hybrid retrievers (e.g., ColBERT, SPLADE) or merge BM25 with vector scores. Rank via linear fusion or learned re-ranker.

922. Designing QA with Search → Rerank → Generate

• Pipeline:

  • Search: retrieve k passages

  • Rerank: cross-encoder filters top-n

  • Generate: LLM answers with citations from context.

923. Measuring Latency, Grounding, Recall

• Latency: avg search + generation time Grounding: citation match rate Recall: % of gold answer-supporting docs retrieved.

924. Handling Irrelevant Passages

• Score passages for relevance. Filter via semantic thresholding or hallucination classifiers before passing to LLM.

925. Intent Detection for Query Reformulation

• Classify query type (navigational, informational, transactional). Use LLM to suggest improved formulations or disambiguations.

926. Storing Feedback for Fine-tuning

• Store query → feedback → doc relevance triplets. Use for retrieval model fine-tuning or prompt tuning (RLHF or DPO).

927. Cross-Encoder Reranking Use Cases

• Use when precision is critical (e.g., legal or medical search). Trades off latency for better context accuracy.

928. Semantic Deduplication of Results

• Use embedding similarity (cosine) and remove near-duplicates. Can also cluster and show summaries.

929. Architecture for Search + Chat Hybrid

• Components:

  • Retriever + Memory

  • Reranker

  • LLM + Tool use

  • Stateful memory store Enable seamless transition from retrieval to multi-turn.

930. Testing for Hallucinated Citations

• Parse generated citations → Match to source

  • If fabricated or misattributed, flag. Use citation classifiers to score hallucination risk.

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931. Evaluating Translation in Low-resource Languages

• Use BLEU/ChrF scores + human raters. Validate cultural meaning, not just literal accuracy. Reference FLORES benchmarks.

932. Role of Locale Embeddings

• Embed cultural, linguistic, and domain-specific context. Improves retrieval relevance and translation accuracy.

933. Tone Consistency Across Languages

• Prompt models with tone tags (e.g., formal, playful). Use tone evaluation LLMs post-translation.

934. Preserving Names, Units, Idioms

• Use tags like <NAME> or <IDM>. Apply post-processing or constrain model with glossaries and domain dictionaries.

935. Fine-tuning on Bilingual Support Logs

• Align source-target turns. Train on pairs using supervised translation loss. Add correction feedback for robustness.

936. Detecting Cultural Insensitivity

• Use moderation LLMs trained on offensive/culturally risky content. Include culture-specific lexicons and flags.

937. Region-Specific Prompt Templates

• Create templates with country-specific spellings, greetings, references. Store templates by locale key.

938. Transliteration vs. Translation vs. Localization

• Transliteration: Phonetic spelling Translation: Language conversion Localization: Cultural adaptation (e.g., changing “football” to “cricket”).

939. Handling Code-Mixing (e.g., Hinglish)

• Train on mixed-language corpora. Use multilingual tokenizers and apply entity-aware prompting.

940. Evaluating Culturally Appropriate Phrasing

• Use cultural review agents or annotators. Score based on alignment with local idioms, politeness, and tone expectations.

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941. Handling Failed Completions Gracefully

• Detect via timeout, empty output, or error codes. Respond with retry or safe fallback message: “Let me recheck that...”

942. Multi-step Workflow Retry Logic

• Isolate stages as idempotent. Wrap each in try-catch with backoff and retry counters. Log partial progress.

943. Fallback to Search-Based Answers

• If model fails or outputs low-confidence response, reroute query to RAG or static KB. Show source-backed snippets.

944. Monitoring Token Quota Exhaustion

• Track per-user/session token usage. Alert or degrade gracefully if nearing quota. Use OpenAI usage API or internal budget manager.

945. Caching Strategies

• Cache by prompt hash or retrieval fingerprint. Store both final answers and intermediate chunks (retrieval or summary).

946. Graceful Degradation to Static Responses

• Maintain static FAQ/heuristic lookup layer. Route to it during model outage or latency spike.

947. Validating Retry-Worthy Outputs

• Use LLM-based output scoring or anomaly detection. Retry if incoherent, out-of-spec, or flagged by rule filters.

948. Cross-provider Redundancy Strategy

• Use abstraction layer (e.g., LangChain, LlamaIndex) to switch between OpenAI, Anthropic, Mistral. Fallback if one fails.

949. Guardrails Against Silent Failures

• Include heartbeat checks, completion length validation, logging for every call. Monitor for abnormal token patterns.

950. Circuit Breakers vs. Retries vs. Escalation

• Circuit Breaker: Stops further calls on repeated failure Retry: Temporary, short-term Human Escalation: Triggered after persistent failure or critical confidence drop.

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